INVESTIGATING MEDIATORS of CHANGE in PROLONGED EXPOSURE THERAPY and SERTRALINE for CHRONIC PTSD by ALLISON L. BAIER, B.S. Submit

Home , Exposure therapy, Habituation

INVESTIGATING MEDIATORS OF CHANGE IN PROLONGED EXPOSURE

THERAPY AND SERTRALINE FOR CHRONIC PTSD

ALLISON L. BAIER, B.S.

Submitted in partial fulfillment of the requirements for the degree of

Master of Arts

Department of Psychological Sciences

CASE WESTERN RESERVE UNIERSITY

August, 2018

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Allison L. Baier

candidate for the degree of Master of Arts *.

Committee Chair

Norah Feeny, Ph.D

Committee Member

Julie Exline, Ph.D.

Committee Member

Arin Connell, Ph.D.

Date of Defense

May 25, 2018

*We also certify that written approval has been obtained for any proprietary material contained therein.

TABLE OF CONTENTS

List of Tables ……………………………………………………………………………..5

Acknowledgements ……………………………………………………………………….6

Abstract …………………………………………………………………………………...7

Introduction……………………………………………………………………………...... 8

Mechanisms and Mediators of Prolonged Exposure Therapy …………………………..10

Understanding Concepts…………………………………………………………10

Prolonged Exposure Therapy ……………………………………………………13

Emotion Processing Theory of PTSD………………………………..…………..14

Between Session Habituation as a Mediator……………………………………..15

Cognitive Theories of PTSD……………………………………………………..18

Negative Posttraumatic Cognition Change as a Mediator……………………….20

Nonspecific Treatment Factors…………………………………………………..21

Therapeutic Alliance as a Mediator……………………………………………...21

Mechanisms/Mediators: Needed Comparisons with Other Treatments…………………24

SSRIs and Putative Psychotherapy Mediators…………………………………...25

Summary ………………………………………………………………………………...27

Aims and Hypotheses……………………………………………………………………28

Method…………………………………………………………………………………...29

Participants……………………………………………………………...………..29

Measures…………………………………………………………………………31

Procedure………………………………………………………………………...32

Overview of Treatment……………………………………………………….….33

Data Analytic Plan……………………………………………………………………….34

Results …………………………………………………………………………………...38

Habituation and PTSD Symptom Change ………………………………………39

Alliance and Habituation Change ……………………………………………….40

Alliance and PTSD Symptom Change …………………………………………..41

Alliance and Belief Change ……………………………………………………..42

Discussion………………………………………………………………………………..44

Clinical Implications …………………………………………………………….49

Research Implications …………………………………………………………...52

Limitations ………………………………………………………………………53

Conclusions ……………………………………………………………………...55

References………………………………………………………………………………..70

List of Tables

Table 1. Demographic Characteristics in Full Sample and by Treatment Type ………...58

Table 2. Means and Standard Deviations of Patients’ Raw Process Variable and PSS-SR Scores at Each Session for Patients in Both Conditions, Combined, and in Each Condition ……………………………………………………………………………..…59

Table 3. Means and Standard Deviations of the Within- and Between-Patient Scores in Both Conditions Combined and in Each Condition ……………………………………..60

Table 4. Correlations among Between-Patient (below diagonal) and Within-Patient (above diagonal) process variables and PTSD symptoms in Both Conditions Combined and in Each Condition Separately ……………………………………………………….61

Table 5. Time Lagged Multilevel Regressions of Habituation (Mean Subjective Units of Distress; SUDs) and Posttraumatic Stress Disorder (PTSD) Symptoms (PTSD Symptom Scale-Self Report; PSS-SR) ……………………………………………………………..62

Table 6. Time Lagged Multilevel Regressions of habituation (Mean Subjective Units of Distress; SUD) and Posttraumatic Stress Disorder (PTSD) Symptoms (PTSD Symptom Scale-Self Report; PSS-SR) in the Aggregated Model and Disaggregated Model (Within and Between Effects)…………………………………………………………………….63

Table 7. Time Lagged Multilevel Regressions of Habituation (Mean Subjective Units of Distress; SUD) and Alliance (Working Alliance Inventory; WAI)……………………..64

Table 8. Time Lagged Multilevel Regressions of Habituation (Mean Subjective Units of Distress; SUDs) and Alliance (Working Alliance Inventory; WAI) in the Aggregated Model and Disaggregated Model (Within and Between Effects)……………………….65

Table 9. Time Lagged Multilevel Regressions of Alliance (Working Alliance Inventory; WAI) and Posttraumatic Stress Disorder (PTSD) Symptoms (PTSD Symptom Scale-Self Report; PSS-SR) ………………………………………………………………………...66

Table 10. Time Lagged Multilevel Regressions of Alliance (Working Alliance Inventory; WAI) and Posttraumatic Stress Disorder (PTSD) Symptoms (PTSD Symptom Scale-Self Report; PSS-SR) in the Aggregated Model and Disaggregated Model (Within and Between Effects)…………………………………………………………………………67

Table 11. Time Lagged Multilevel Regressions of Alliance (Working Alliance Inventory; WAI) and Beliefs (Post-Traumatic Cognitions Inventory; PTCI) ...... ……..68

Table 12. Time Lagged Multilevel Regressions of Alliance (Working Alliance Inventory; WAI) and Beliefs (Post-Traumatic Cognitions Inventory; PTCI) in the Aggregated Model and Disaggregated Model (Within and Between Effects)……………………………….69

Acknowledgements

Preparation of this project was supported by grants to Drs. Zoellner and Feeny from the National Institute of Mental Health (R01 MH066347, R01 MH066348),

Supplemental Grant from Pfizer, Inc. and the William T. Dahms, M.D. Clinical Research

Unit, funded under the Cleveland Clinical and Translational Science Award (M01

RR00080 and UL1 RR024989).

I would like to express my sincere gratitude to my advisory, Norah Feeny, Ph.D., for her contributions and support of this research project. I would also like to thank my committee members, Julie Exline, Ph.D., and Arin Connell, Ph.D., for their guidance and valuable insights throughout this process. Finally, I would like to thank my fiancé, family, and friends for their endless love, support, and encouragement.

Investigating Mediators of Change in Prolonged Exposure Therapy and Sertraline for

Chronic PTSD

Abstract

ALLISON L. BAIER

While efficacious psychological treatments for PTSD exist, little is known about the mechanisms by which treatment leads to symptom reduction. A better understanding of these processes is imperative for personalizing treatment and augmenting clinical outcomes. The current study examined three putative mediators—between session habituation, negative trauma-related belief change, and the therapeutic alliance—with one another and with PTSD symptom change in 144 individuals receiving prolonged exposure (PE) or sertraline for PTSD. Using time-lagged mixed regression models that disaggregated within- and between-patient effects, three significant findings emerged: improvements in alliance predicted next session PTSD symptom reduction in both treatments, improvements in alliance predicted next session belief change in PE, and belief change predicted next session alliance improvements in both treatments. Notably, habituation was not a significant predictor of next session PTSD symptom change or alliance. Findings are discussed in the context of clinical implications and methodological implications for mechanism research.

Keywords: PTSD, mediators, habituation, cognitive beliefs, alliance, PE, sertraline

Investigating Mediators of Change in Prolonged Exposure Therapy and Sertraline For

Chronic PTSD

Posttraumatic stress disorder (PTSD) is categorized under the Trauma- and

Stressor-Related Disorders category of the Diagnostic and Statistical Manual of Mental

Disorders (Association, 2013) and develops in a significant minority of individuals following exposure to a traumatic event. National epidemiological surveys suggest that over half of the population in the U.S. experiences at least one traumatic event (Kessler,

Sonnega, Bromet, Hughes, & Nelson, 1995); however, only seven to ten percent of these are estimated to develop PTSD in their lifetime (Breslau, 2009; Kessler et al., 2005;

Koenen et al., 2017). Rates of PTSD are higher among high-risk trauma exposed groups including those of low socioeconomic status living in urban environments (Gillespie et al., 2009), survivors of rape (Breslau, 2009; Kessler et al., 2005; Resnick, Kilpatrick,

Dansky, Saunders, & Best, 1993), military service members (Hoge et al., 2004), and those in war-stricken areas (de Jong et al., 2001). Indeed, there is robust support for a dose-response relationship between the severity of trauma exposure and severity of PTSD psychopathology, with interpersonal violence emerging as one of the greatest risk factors for the development of PTSD (Breslau, 2009; Resnick et al., 1993). PTSD has profound public health consequences (Atwoli, Stein, Koenen, & McLaughlin, 2015; Kessler, 2000) and efforts are underway to both reduce trauma exposure (Magruder, Kassam-Adams,

Thoresen, & Olff, 2016), and develop treatments for those who develop PTSD (Foa,

Hembree, & Rothbaum, 2007; Resick, Manson, & Chard, 2008).

Fortunately, efficacious treatments for PTSD exist. Prolonged exposure (PE) and sertraline are two evidence-based treatments for PTSD (Foa et al., 2007; Jonas et al.,

2013; Lee et al., 2016). While a large body of evidence supports the efficacy of psychotherapy and pharmacotherapy interventions for PTSD (Powers, Halpern,

Ferenschak, Gillihan, & Foa, 2010; Watts et al., 2013), a substantial minority of individuals experience an incomplete or non-response to treatment (Bradley, Greene,

Russ, Dutra, & Westen, 2005; Steenkamp, Litz, Hoge, & Marmar, 2015). A recent meta- analysis revealed only 67% of those who completed treatment no longer met criteria for

PTSD post treatment; one third of treatment completers retained their diagnosis and thus had residual symptoms post treatment indicative of attenuated treatment efficacy

(Bradley et al., 2005). Additionally, though number of treatment sessions is associated with PTSD symptom reduction (Bradley et al., 2005; Watts et al., 2013), treatment dropout is also common with rates ranging from 8.8-28.5% (Hembree et al., 2003; Imel,

Laska, Jakupcak, & Simpson, 2013; Swift & Greenberg, 2014), resulting in significant costs including wasted resources, worse outcomes, and patient and therapist demoralization (Barrett, Chua, Crits-Christoph, Gibbons, & Thompson, 2008). Though rates of dropout for PTSD treatment are on par with rates for other anxiety and depression disorders (Hans & Hiller, 2013; Hofmann & Suvak, 2006; Keijsers,

Kampman, & Hoogduin, 2001), a more nuanced understanding of why treatments work, or the mechanisms by which interventions lead to change, would enable clinicians to better optimize the therapeutic elements of treatment that lead to change and better understand the factors that lead to dropout or nonresponse (Kazdin, 2007; Murphy,

Cooper, Hollon, & Fairburn, 2009). Understanding the processes underlying change is critical for optimizing patient outcomes and reducing the burden of PTSD. In this study, the temporal relationships between three putative mechanisms and with outcome in a

randomized control trial of patients treated with PE or sertraline for PTSD was examined.

Mechanisms and Mediators of Prolonged Exposure

Understanding Concepts

As evidence of the efficacy of various psychotherapies and pharmacotherapies mounts, research is needed to unveil change processes, or why treatment works, to continue refining treatments for more personalized care. Researchers have identified three key areas of process research: (1) Course of change (is the change linear, nonlinear?); (2) Moderators of change (for whom and under what conditions/treatments does change occur?); and (3) Mediators of change (why and how is change occurring?)

(Laurenceau, Hayes, & Feldman, 2007). An understanding of the course of change may reveal where therapists should focus their efforts in psychological treatments or, during what time of pharmacological treatment psychiatrists should zero in on medication compliance. Additionally, the course of change may help researchers and clinicians alike identify differences between treatment responders and non-responders that can influence targets for treatment retention techniques (Laurenceau et al., 2007). Moderators will aid therapists in personalizing the delivery of an evidence-based treatment and matching patients to the interventions that will best suit their needs and characteristics (Kraemer,

Wilson, Fairburn, & Agras, 2002). Finally, mediators of change are important for elucidating potential mechanisms of change through which a treatment has its effects on symptom reduction. Not all mediators are mechanisms; mediators could be covariates or proxies for true mechanisms. However, this critical first step of identifying mediators of change is necessary for answering questions related to why and how a treatment exerts its effect. As noted above, even the most efficacious treatments do not help all patients;

identifying mediators of change can help ensure future treatments or revised treatments continue to have the “essential” components and do not contain unnecessary components

(Kraemer et al., 2002). Further, understanding these change processes could help improve our understanding of disorders and the variables that may maintain psychopathology

(Laurenceau et al., 2007).

While the study of “mechanisms” in psychotherapy research is increasingly common, most research actually examines relationships between hypothesized change processes and outcome variables through mediation analyses. Mediators are variables that statistically account for the relationship between an independent and dependent variable

(Baron & Kenny, 1986); however, a mere identification of a mediator does not necessarily explain the reason or cause for change. Indeed, in addition to the statistical requirements for mediation, Kazdin (2007) identified six other criteria that ought to be met if inferences are going to be drawn about an intervening process accounting for change. Perhaps the most important criteria is temporal precedence, wherein the mediator temporally precedes the changes in the outcome variable (Collins, 2006; Kazdin, 2007;

Laurenceau et al., 2007). With a timeline in mind, studies must therefore measure the proposed mediator and outcome variable multiple times over the course of treatment and subsequently examine the relationship using lagged models. Further, studies must also examine the possibility of reverse causation to definitively show that the mediator truly precedes the outcome variable and not vice versa. Thus, though the terms ‘mediation’ and ‘mechanism’ are often used interchangeably, this loose terminology has potentially negative consequences for the field’s understanding of change processes and leads to conceptual ambiguity, and at times, overstated conclusions about causal processes

(Kazdin, 2007).

Time varying predictor variables, examined such as through lagged designs, can additionally help distinguish between-patient and within-patient effects. Between-patient effects refer to interindividual processes wherein change is investigated between groups of individuals, whereas within-patient effects refer to intraindividual processes wherein change is investigated within individual patients (Curran & Bauer, 2011). Research has largely made conclusions about within-patient processes from the analysis of between- patient data (Curran & Bauer, 2011). While the interpretation of within-patient effects from between-patient data are important in progressing science forward, the presence of a between-patient relationship is not adequate and sufficient evidence to claim a within- patient effect of a mediator on outcome. To illustrate using a hypothetical example, a significant relationship between self-reports of friendliness measured at session 2 and outcome in ten weeks of psychotherapy could actually be representative of a proxy for some other patient/subject level (between-patient) variable such as personality or diagnosis. If a researcher fails to recognize the limitations of the between-patient analysis, they could inadvertently conclude that early perceptions of one’s friendliness is necessary for treatment outcome and spend effort building their patient’s perception of friendliness. Thus, researchers must examine the within-patient variability of the relationship (i.e., disaggregating the within- and between-patient effects) between two variables for the within-patient variability identifies a relation that cannot be attributed to any stable patient characteristics (between-patient variability). It should be noted though that lagged designs in and of themselves are not perfect for the separation of effects; methods such as person-mean centering (Wang & Maxwell, 2015) or the use of latent

variables (Curran, Lee, Howard, Lane, & MacCallum, 2012) are necessary. The disentanglement of between-patient and within-patient effects is critical for appropriate conclusions from process research (Curran & Bauer, 2011). By truly understanding the processes underlying change, the field will be better equipped to optimize current treatments and personalize them to each patient.

Prolonged Exposure Therapy

PE is an empirically-supported cognitive behavioral therapy for PTSD (Powers et al., 2010; Watts et al., 2013). While other efficacious cognitive behavioral therapies exist for the treatment of PTSD such as cognitive processing therapy (Resick & Schnicke,

1993) and cognitive therapy (Ehlers, Clark, Hackmann, McManus, & Fennell, 2005), PE is among the best studied and recommended first line interventions by clinical practice guidelines (IOM, 2008). PE consists of 10 weekly 90-minute treatment sessions that include psychoeducation about common reactions to trauma, breathing retraining, prolonged (repeated) imaginal exposure to trauma memories, repeated in vivo exposure to situations the patient is avoiding due to trauma-related fear, and emotional processing regarding thoughts and feelings related to the two exposure exercises. Homework is assigned in each session to promote exposure and facilitate learning and processing and discussed at the following session (Foa et al., 2007). Due to its efficacy, PE has been the subject of wide dissemination efforts (Foa, Cashman, Jaycox, & Perry, 1997; Foa,

Gillihan, & Bryant, 2013; McLean & Foa, 2013); however, why PE is efficacious remains to be elucidated (Cooper, Clifton, & Feeny, 2017). As noted above, a greater understanding of change processes will enable researchers and clinicians to tailor treatments to patients with greater specificity.

Emotion Processing Theory of PTSD

PE is rooted in emotional processing theory (Foa & Kozak, 1986). Integrating and building on the work of Lang’s bioinformational model of fear (1977), EPT conceptualizes fear as an emotion structure in one’s memory that includes stimuli or representations of the feared stimuli (e.g., a hoodie resembling the one worn by an assailant), fear responses (e.g., anxiety), and meaning associated with the stimuli and responses (e.g., “The world is unsafe.” “I am in danger”). While normally adaptive, the fear structure can become maladaptive when it includes excessive, erroneous associations with neutral stimuli that do not pose actual threat. For example, a woman who is sexually assaulted in a parking lot by a man in a hoodie may come to fear all people wearing hoodies and being alone in a parking lot, illustrative of the erroneous associations within her fear structure that influence her behavior (e.g., avoiding people wearing hoodies or parking lots) and cognitions about the self, others, and world (“I can’t handle this.”

“People aren’t to be trusted.” “The world is dangerous.”) that maintain pathology. The ensuing emotional and physiological distress from confrontation with the memory or trauma reminders promotes avoidance which is negatively reinforced by a reduction in fear. The reduction in fear thus promotes further avoidance, maintaining PTSD symptoms and the maladaptive fear structure. It is thus theorized that those individuals who experience a natural recovery after exposure to a traumatic event do so through effective emotional processing such as by talking about the trauma with others or through natural exposures to incompatible information with fear structures that disconfirm common post trauma associations such as “the world is entirely dangerous” or “I am completely at fault for what happened” (Foa, Huppert, & Cahill, 2006; Foa & Kozak, 1986).

The theorized development of the PTSD has important implications for treatment.

Because the precipitant or cause of PTSD is known (i.e., the traumatic event), therapy can work to disrupt the pathological fear structure through targeted, prolonged exposures to the feared memory through imaginal recounts of the trauma (i.e., imaginal exposure) and direct exposures to avoided stimuli (i.e., in-vivo exercises such as touching a hoodie or returning to the parking lot of the assault). Imaginal exposure is a key component of PE that seeks to first, activate the fear structure and second, modify the fear structure through habituation to the trauma memory within and across the treatment sessions. With both imaginal exposure and in-vivo exposure, evidence discounting the beliefs of the pathological fear structure (e.g., “Nothing bad happened to me when I revisited the parking lot. Maybe the world isn’t all unsafe. I didn’t die from my anxiety.”) are accumulated and the pathological fear structure is modified to be more adaptive (Foa &

Kozak, 1986).

Between-Session Habituation as a Mediator

As noted above, one of the central therapeutic components of PE is imaginal exposure. During imaginal exposure, which starts in session 3, patients are asked to repeatedly retell the trauma memory and to use as much detail as possible including sights, sounds, smells, thoughts, feelings, and physical sensations in an effort to fully activate the fear structure by engaging the patient with the trauma memory. The clinician monitors the patient’s distress every five minutes on a scale from 0 (no distress) to 100

(maximum distress) known as the Subjective Units of Distress Scale (SUDs; Wolpe &

Lazarus, 1966). SUDs serve as a proxy measure of fear activation; as previously described, the fear structure must be activated to allow for corrective information.

Habituation to the memory, evidenced by a reduction in anxiety within and across therapy sessions through the monitoring of SUDs, is regarded as evidence of fear structure modification and emotional processing. By experiencing a reduction in distress overtime, patients learn that their trauma memory is no longer harmful, that they can tolerate the distress, and that their previously held negative trauma-related beliefs about themselves, others, and the world are inaccurate (Foa & Kozak, 1986; Gillihan & Foa,

2011).

While a number of processes have been theorized to underlie treatment change in

PE (Cooper, Clifton, et al., 2017; Foa et al., 2006), habituation, or decreased response to trauma cues or reminders within a therapy session (within-session habituation) and across the 10 sessions of treatment (between-session habituation) examined via SUDs, have arguably been the most studied as potential processes of change and predictors of treatment response (Cooper, Clifton, et al., 2017; van Minnen & Hagenaars, 2002). It should be noted that significant conceptual ambiguity revolves around the term

“habituation” wherein some conceptualize the decreased response to trauma cues in exposures as reflective of learning processes, or extinction, rather than non-learning processes like habituation (Myers & Davis, 2007). While fear inhibition models supporting extinction processes have emerged (Kindt, 2014; Milad & Quirk, 2012),

“habituation” is used extensively in clinical work and research, is conceptualized in EPT, and will be used throughout this paper.

Within-session habituation has very little support in the PTSD treatment literature. Among the eight studies investigating within-session habituation (de Kleine,

Smits, Hendriks, Becker, & van Minnen, 2015; Harned, Ruork, Liu, & Tkachuck, 2015;

Jaycox, Foa, & Morral, 1998; Nacasch et al., 2015; Pitman, Orr, Altman, & Longpre,

1996; Sripada & Rauch, 2015; van Minnen & Foa, 2006; van Minnen & Hagenaars,

2002), only one (de Kleine et al., 2015) found support for within-session habituation as a potential mechanism of change in PE. The authors found within-session habituation

(conceptualized by the authors as “extinction”) was related to lower self-report PTSD symptom scores at the next session and to post-treatment symptoms (de Kleine et al.,

2015). Further, the only two studies to use more robust and sophisticated statistical techniques—cluster analysis and hierarchical linear modeling—also failed to find a relationship between within-session habituation and outcome (Jaycox et al., 1998;

Sripada & Rauch, 2015). Thus, there is minimal evidence for the importance of within- session habituation for treatment gains.

In contrast, between-session habituation, typically measured by comparing peak or mean SUDs from the first exposure to the final exposure, has been identified as a predictor of superior outcomes across varied samples and trauma characteristics

(Gallagher & Resick, 2012; Harned et al., 2015; Nacasch et al., 2015; Rauch, Foa, Furr,

& Filip, 2004; van Minnen & Foa, 2006). One study investigating the relationship between just the first and second imaginal exposure session found patients with greater symptom improvement experienced greater between-session habituation (van Minnen &

Hagenaars, 2002). Two studies have examined mean between-session change in peak

SUDs similarly finding improved outcome (de Kleine et al., 2015; Rothbaum et al., 2014) and two additional studies utilizing robust statistical techniques also found strong relationships between outcome and between-session habituation (Jaycox et al., 1998;

Sripada & Rauch, 2015). However, a recent study indicated over two-thirds of those in

PE did not display a reliable change in distress (i.e., peak or mean SUDs) over the course of treatment, yet still benefitted from PE (Bluett, Zoellner, & Feeny, 2014), though individuals who did experience reliable change in peak and mean SUDs over the course of treatment (i.e., between-session habituation) made larger clinical gains than those who did not experience such a reduction (Bluett et al., 2014). Thus, the role and necessity of habituation for treatment gains continues to be evaluated and debated as a potential mediator and important change process. Additionally, while twelve studies have examined between-session habituation as a predictor of outcomes, only three have used multiple assessments across treatment (de Kleine et al., 2015; Jaycox et al., 1998; Sripada

& Rauch, 2015) and only one of those (de Kleine et al., 2015) investigated session by session changes. Thus, there has been limited study examining the temporal relationship between habituation and outcome. Perhaps patients experience a change in symptoms and as a result experience lower levels of distress during their imaginal exposures rather than habituation preceding symptom change. Further work examining the temporal relationships between habituation and outcome is necessary to elucidate how habituation interacts with treatment outcome as a potential mediator of change in PE.

Cognitive Theories of PTSD

Cognitive theories (Ehlers & Clark, 2000; Foa & Rothbaum, 1998; Janoff-

Bulman, 1992; Resick & Schnicke, 1993), rest on the assumption that maladaptive interpretations or appraisals of the traumatic event (e., “I am to blame” or “The world is completely dangerous”) and trauma sequelae including symptoms and emotional responses (e.g., “my reactions mean I’m losing it”), others’ reactions (e.g., being told it was one’s fault), and consequences of the trauma on quality of life (e.g., job loss due to

missed work from avoidance), produce a pervasive sense of threat that promotes avoidance and overgeneralized fear. The theory also suggests that recall of trauma memories can be fragmented and poorly organized which, coupled with physiological reactions such as involuntary intrusive memories (e.g., flashbacks), contribute to a reciprocal relationship between the nature of the trauma memory and the appraisals of the traumatic event and its sequelae (Ehlers & Clark, 2000). When reminded of the traumatic event, individuals with PTSD are thought to recall a biased recollection of their appraisals of the event and sequelae that support and are consistent with the appraisals. For example, a rape victim who believes her assault was her fault may recall the unhelpful or unfriendly responses from the police and physicians in the emergency room but not recall her friends’ support and love in the months following the assault. These appraisals can then lead to maladaptive behavioral strategies and cognitive processing strategies that create a negative feedback loop maintaining PTSD. It is thus theorized that avoidance is a consequence and not a cause of a persistent sense of threat (Ehlers & Clark, 2000). EPT additionally discusses how negative trauma-related beliefs can hinder recovery by maintaining avoidance behaviors (Foa et al., 2006); such beliefs have therefore been an increasing focal point of PTSD research and a proposed putative mediator of change warranting additional study (LoSavio, Dillon, & Resick, 2017). It may be that helping patients reappraise their memory of the traumatic event and its sequelae is a key recovery process. Research investigating how changes in beliefs affect symptom change will help elucidate the role of negative beliefs in PTSD pathology and treatment.

Negative Posttraumatic Cognitions as a Mediator

Negative trauma related beliefs about oneself, others, and the world are a peritraumatic risk factor for the development of PTSD (Lancaster, Melka, & Rodriguez,

2011; Shahar, Noyman, Schnidel-Allon, & Gilboa-Schechtman, 2013). Additionally, lack of positive social support following trauma has emerged as one of the strongest predictors of PTSD (Brewin, Andrews, & Valentine, 2000; Ozer, Best, Lipsey, & Weiss, 2008).

Many theories postulate that social support following trauma can help promote natural recovery by helping to buffer against negative appraisals and encourage adaptive, healthier ways of thinking about the trauma (Cohen & Wills, 1985). Negative posttraumatic cognitions diminish alongside PTSD symptoms (Foa & Rauch, 2004;

Hagenaars, Van Minnen, & De Rooij, 2010; Nacasch et al., 2015) and have been subsequently recognized as a potential mediating variable related to change processes in

PTSD treatment. Recent research using advanced statistical methods that consider time sequencing (e.g., lagged models) has found that negative posttraumatic belief change mediates symptom change (Cooper, Zoellner, Roy-Byrne, Mavissakalian, & Feeny, 2017;

Kleim et al., 2013; Kumpula et al., 2017). These studies have provided robust support for negative belief change as a mediator of PE suggesting changes in how one thinks about oneself, others, and the world may be critical for reducing PTSD symptomatology.

While evidence exists for the mediating properties of negative belief change, only one study to date has examined the relationship between negative belief change and habituation (Nacasch et al., 2015). The authors found that negative belief change was not correlated with between session habituation though both were trend-level predictors of

PTSD symptom improvement. While these results provide some support that negative

belief change and habituation are separate processes (Nacasch et al., 2015), what remains to be considered is whether these processes influence or interact with one another.

Investigating multiple putative mediators at one time allows for an examination of which mediator may exert a greater effect on outcome and, additionally, whether one mediator precedes and/or is related to another mediator to contribute to treatment outcome.

Non-specific Treatment Factors

Research has often classified potential mechanisms into either “nonspecific” factors or “specific” factors. Nonspecific, or common, factors are elements of psychotherapy that may be responsible for change but that are largely independent of the therapy in question, whereas specific factors are directly tied to the specifics of the therapy (DeRubeis, Brotman, & Gibbons, 2005; Kazdin, 1979). While treatment-specific factors have dominated much of the process and mechanism research literature, common factors such as the therapeutic alliance are additionally important to examine.

Methodologists have argued that nonspecific treatment factors cannot be desegregated from specific factors for the simple delivery of an intervention cannot occur without introducing some common elements such as provider empathy, compassion, and warmth, and patient perceptions of treatment credibility and expectancy (Wilkins, 1979).

However, the inherent overlap between nonspecific and specific factors speaks to the need to evaluate nonspecific factors as the primary variable of interest so as to compare and better understand differences in specific and nonspecific processes of change and how the factors may interact.

Therapeutic Alliance as a Mediator

The therapeutic alliance constitutes the relationship between the psychotherapist

and the patient. While its conceptualization dates back to Freud (1912/1958), Bordin

(1979, 1994) proposed the concept of the “working alliance” focused on the collaborative nature of the therapeutic relationship through treatment. A stronger therapeutic alliance has consistently been found to be associated with better treatment outcomes (e.g., symptom reduction, quality of life improvements) and greater therapeutic change across psychotherapies (Horvath, Del Re, Flückiger, & Symonds, 2011), including exposure- based therapies (Capaldi, Asnaani, Zandberg, Carpenter, & Foa, 2016; McLaughlin,

Keller, Feeny, Youngstrom, & Zoellner, 2014). Of note, only four studies have directly investigated the therapeutic alliance in PE for PTSD (Capaldi et al., 2016; Hoffart,

Øktedalen, Langkaas, & Wampold, 2013; Keller, Zoellner, & Feeny, 2010; McLaughlin et al., 2014).

A common assumption is that the therapeutic alliance precedes symptom change; however, the predictive nature of alliance measured at one time point says little about its potential causal role in psychotherapy (Kazdin, 2007). Perhaps early in treatment, patients get better and as a result form a more positive alliance with their therapist.

Additionally, investigating alliance as a predictor of outcome examines between-subject effects, not within. As noted above, change process research must study the within- subject effects if true mechanistic conclusions are to be drawn (Curran & Bauer, 2011).

As noted by Falkenström, Granström, and Holmqvist (2013), a between-subject relationship between a given time point of alliance with treatment outcome may in fact be a proxy for some other subject level variable, such as temperament or diagnosis.

The limited research investigating the relationship between repeated measures of alliance and symptom change has yielded mixed results (Crits-Christoph, Gibbons,

Hamilton, Ring-Kurtz, & Gallop, 2011; Crits-Christoph, Gibbons, & Hearon, 2006;

Falkenström et al., 2013; Strunk, Brotman, & DeRubeis, 2010; Strunk, Cooper, Ryan,

DeRubeis, & Hollon, 2012; Tasca & Lampard, 2012). Tasca and Lampard (2012) found evidence for a reciprocal causal model where change in alliance predicted symptom change and symptom change also predicted alliance in a group-based day treatment program for eating-disordered individuals. Another study found support for the same reciprocal causal model (Falkenström et al., 2013); however, the study included data from a primary care clinic treating multiple disorders (e.g., anxiety, depression, grief) through various therapies (e.g., psychodynamic, cognitive behavior therapy, interpersonal), and thus the results may not provide a nuanced picture of the alliance- outcome relationship for different disorders or therapy orientations. A study conducted by

Crits-Christoph and colleagues (2011) found alliance predicted symptom change in the subsequent session of a 16-week treatment but the reverse (symptom change predicting later alliance) was only found later in treatment between sessions 10 and 16. Finally,

Strunk and colleagues (2010; 2012) found no relationship between alliance on later symptom change in cognitive therapy for depression. Thus, there has been limited research investigating the temporal within-subject relationships between therapeutic alliance and symptom change. Further, studies have involved a number of treatments for varying psychopathologies; yet no study has directly investigated the temporal relationship between the therapeutic alliance and subsequent symptom change for PE or any exposure-based therapy. Thus, the potential role of alliance as a change process has not been systematically evaluated in PE; whether alliance precedes symptom improvement, or whether it is simply a byproduct of experiencing a reduction in

symptoms, remains to be elucidated.

Alternatively, perhaps alliance bears no relationship with outcome but rather interacts or facilitates other mechanistic processes such as habituation or belief change.

Only one study investigated such interactive processes. Researchers found a quadratic relationship between observed therapeutic alliance and within-session habituation in socially anxious patients, with moderate levels of alliance associated with the greatest amount of within-session habituation (Hayes, Hope, VanDyke, & Heimberg, 2007).

However, the temporal sequencing of these constructs, and the effect of between-session habituation, was not examined. Given the importance of establishing a timeline for mechanistic processes, and the relative importance of between-session habituation in exposure-based therapies, future work needs to be conducted to further investigate the potential relationships between these putative mediators. Additionally, no research has investigated the relationship between alliance and belief change in PE. It is quite plausible that the therapeutic alliance facilitates belief change by providing an opportunity for collaborative empiricism (Overholser, 2011; Tee & Kazantzis, 2011) and beginning evidence in support of more realistic cognitions (e.g., “My therapists doesn’t think the rape was my fault and supports me- maybe there are some good, friendly people in the world”). Alternatively, perhaps the formation, maintenance, and growth of therapeutic alliance is a result of changes in negative beliefs. A clear understanding of these processes and the associated temporal relationships between them will aid in better understanding how treatments work.

Mechanisms/Mediators: Needed Comparisons with Other Treatments

Randomized controlled trials offer a unique opportunity for the investigation of

mediators, particularly when the treatment conditions are equally effective for it allows for direct comparisons of the putative mediators of each condition (Kraemer et al., 2002;

Laurenceau et al., 2007; Murphy et al., 2009). Sertraline, a selective serotonin reuptake inhibitors (SSRI), is one of only two medications approved by the U.S. Food and Drug

Administration (FDA) for treatment of PTSD (Friedman & Davidson, 2014). Sertraline has been found to produce clinical global improvement though may produce common side effects such as insomnia, restlessness, nausea, decreased appetite, and daytime sedation (Brady et al., 2000; Davidson, Rothbaum, van der Kolk, Sikes, & Farfel, 2001;

Friedman, Marmar, Baker, Sikes, & Farfel, 2007; Lee et al., 2016; Zohar et al., 2002).

Given the intracellular effects of SSRIs on neuronal deficits that contribute to psychopathology (Duman & Voleti, 2012), specifically depression (Anderson, 2000) and

PTSD (Stein, Seedat, van der Linden, & Zungu-Dirwayi, 2000), the therapeutic action of

SSRIs may be substantially different from that of psychotherapy making it a suitable treatment comparison for the study of process variables in PE. While it is plausible that

SSRIs and psychotherapy exert their effects through different mechanisms, it is also plausible that the two forms of treatment may be more similar than they are dissimilar.

SSRIs and Putative Psychotherapy Mediators

While the effects of SSRIs on habituation to feared stimuli to have yet to be thoroughly examined in those suffering from PTSD, a single dose of escitalopram was found to dose-dependently delay habituation to an acoustic startle response (ASR) in a sample of healthy male subjects (Jensen, Oranje, Wienberg, & Glenthøj, 2007; Oranje,

Wienberg, & Glenthoj, 2011). In another sample of depressed patients, sertraline was found to attenuate habituation to the ASR (Quednow et al., 2004). Additionally, research

investigating the ASR in rats found that paroxetine, but not fluoxetine, interfered with habituation and enhanced startle response (Amodeo et al., 2015). Thus, there are mixed results for the effects of SSRIs on habituation and very limited generalizability to patients with PTSD. We did not examine habituation in the sertraline arm of the RCT and will thus not investigate habituation in the sertraline arm of this study; however, though more research is needed, it is likely SSRIs for PTSD exerts effects through mechanisms other than habituation.

SSRIs have also been found to affect emotional and cognitive processes often most typified as psychological processes (Harmer, 2008). In a study comparing antidepressant therapy to combined antidepressant and cognitive therapy for depression, researchers found few differences in the change of self-reported core beliefs between treatments (Dozois et al., 2014). Further, translational genetic, biochemical, and neuroanatomical research in mice has found evidence for possible overlapping mechanisms of fear extinction and depression (Tronson et al., 2008), suggesting there may be common mechanisms of change on anxiety through the effects of antidepressants such as SSRIs. To evaluate the potential differential role of trauma-related belief change on symptom improvement in patients treated with SSRI monotherapy versus psychotherapy, our group (Cooper, Zoellner, et al., 2017), previously investigated the temporal relationship between belief change and symptom change for patients treated with sertraline or prolonged exposure therapy for PTSD using time-lagged mixed regression models, finding belief change predicted subsequent PTSD symptom improvement more strongly in PE (d = 0.93) compared to the sertraline condition (d =

.35), suggesting that change in beliefs may be a strong mediator in PE treatment

compared to SSRI treatment. While we have conducted work investigating the temporal relationship between belief change and outcome, no study to date has investigated the relationships between belief change and alliance in pharmacotherapy, detailed more fully below.

The importance of therapeutic alliance has consistently been acknowledged in psychotherapies (Horvath et al., 2011), including PE (McLaughlin et al., 2014); however, no studies to date have examined the relationship between the therapeutic alliance and pharmacotherapy for PTSD. Further, only a few have directly studied the relationship between alliance and outcome in other pharmacological treatments, specifically depression (e.g., Krupnick et al., 1996; Weiss, Gaston, Propst, Wisebord, & Zicherman,

1997) and bipolar (e.g., Gaudiano & Miller, 2006). While lack of study certainly does not necessarily imply a lack of importance, it is likely that alliance may serve as a more prominent mediator of change in PE than in sertraline given the greater role therapists serve in psychotherapy compared to psychiatrists in pharmacotherapy.

Summary

Evaluating the temporal relationships of putative mediators is important for understanding the processes through which psychological and pharmacological treatments reduce PTSD symptomatology. In this study, the temporal relationships between habituation and PTSD outcome, therapeutic alliance and habituation, alliance and PTSD outcome, and alliance and belief change were examined in patients with PTSD treated with PE or sertraline. Additionally, as habituation was not evaluated in the sertraline condition, this study only examined the effect of treatment (PE vs. sertraline) on the temporal relationships between therapeutic alliance and PTSD outcome, and

alliance and belief change in patients with PTSD. As detailed in the “Data Analytic

Approach” in the Methods section, we used time lagged repeated-measures regression models to estimate the magnitude of the relationships between variables at the session level and compare the direction of effect (e.g., whether alliance mediates symptom change or vice versa running reverse models). We next disaggregated the raw scores for each variable into scores reflecting within-patient and between-patient variability

(described more fully in the “Data Analytic Approach” section), allowing us to effectively control for all stable between-patient differences by focusing on the potential relation of within-patient processes of change. Again, we ran models in both directions to examine the direction of effect.

Aims and Hypotheses

The current study sought to provide a more nuanced understanding of potential mechanistic processes in two evidence-based treatments for PTSD in a treatment seeking sample of men and women receiving either a psychotherapy (PE) or pharmacotherapy

(sertraline). First, given the predictive nature of between-session habituation and symptom change (Gallagher & Resick, 2012; Nacasch et al., 2015; Rauch et al., 2004), we hypothesized that habituation would precede symptom change in the PE sample.

Second, given past work supporting the predictive nature of alliance (Capaldi et al., 2016;

Keller et al., 2010; McLaughlin et al., 2014) and habituation (e.g., Gallagher & Resick,

2012; Nacasch et al., 2015; Rauch, Foa, Furr, & Filip, 2004) on treatment outcome, we hypothesized that improvements in alliance would predict habituation in PE. Third, in- line with past research (Falkenström et al., 2013; Tasca & Lampard, 2012), we hypothesized that improvements in alliance would predict PTSD symptom change in PE

and sertraline. Additionally, given the more prominent role of the alliance in psychotherapy than in pharmacotherapy, we hypothesized that the effect would be moderated by a treatment and be more robust in PE than in the sertraline condition.

Finally, as previous work has already examined the temporal relationship between cognitive change and symptom change in our sample (Cooper, Zoellner, et al., 2017) and other samples (Kleim et al., 2013; Kumpula et al., 2017), it was hypothesized that alliance would mediate cognitive change in PE and in the sertraline condition. Past research has found greater belief change in PE than in sertraline (Cooper, Zoellner, et al.,

2017) and thus it was also hypothesized that the relationship would be more robust in PE than in the sertraline condition.

Method

Participants

The sample for the current study was drawn from a large randomized controlled trial comparing PE with sertraline for the treatment of chronic PTSD (Zoellner, Roy-

Byrne, Mavissakalian, & Feeny, 2017). The trial consisted of two hundred men (24.5%, n

= 49) and women (75.5%, n = 151) between the ages of 18 and 65 with a primary diagnosis of chronic PTSD. Exclusion criteria were minimal to best mimic clinical populations and included: primary DSM-IV diagnosis other than chronic PTSD; current diagnosis of schizophrenia or other psychotic disorder, medically unstable bipolar disorder, depression requiring immediate psychiatric treatment or with psychotic features; alcohol or substance abuse within the previous three months; severe self-injurious behavior or suicide attempt within the past three months; an ongoing relationship with perpetrator in cases of sexual or physical assault; a change in dose of psychiatric

medication within the past three months; medical contraindication for taking sertraline; or previous non-response to adequate trial of either PE (8 sessions or more) or sertraline (8 weeks, 150 mg/d).

In line with past work (Cooper, Zoellner, et al., 2017; Kleim et al., 2013) and theoretical models (Yang & Maxwell, 2014), only patients who completed a minimum of five treatment sessions were included in this study, resulting in a final sample of 144 patients. Handling missing data, particularly in randomized clinical trials, has long been debated with some researchers calling for analyzing intent-to-treat samples (Leuchs,

Brandt, Zinserling, & Benda, 2017) and others for the use of data imputation methods

(Li, Stuart, & Allison, 2015; Sterne et al., 2009). Rather than a standardized approach for handling missing data, however, methods should be rooted in the question of interest.

Process research seeks to uncover why and how a treatment works. Including patients who dropped out early in treatment or imputing clinical information for those patients, at best, provides little useful information (DeRubeis, Gelfand, German, Fournier, & Forand,

2014) and, at worst, risks diluting or shadowing the effect of the mechanism in question, or causing assumption violations such as with non-random missingness (Yang &

Maxwell, 2014).

Participants in this study had a mean age of 37.7 (SD = 11.8) years. The majority

(70.8%, n = 102) identified as Caucasian with the remaining 29.2% identifying as African

American (16.0%, n = 23), Asian (6.3%, n = 9), American Indian/Alaskan native (2.1%, n = 3), or as “other” (4.9%, n = 7). Primary index traumas reported included adult physical or sexual assault (51.4%), childhood physical or sexual assault (21.7%), accident or natural disaster (15.3%), death or violence to a loved one (5.6%) and combat/war

(2.8%) with an average time of 12.4 years (SD = 13.0) reported since trauma. See Table 1 for demographic characteristic breakdown by treatment type.

Measures

PTSD Symptom Scale – Self-Report (PSSS-SR; Foa et al., 1997; Foa, Riggs,

Dancu, & Rothbaum, 1993) . The PSS-SR is a 17-item self-report measure used to assess

DSM-IV PTSD symptom severity and frequency. Participants completed the measure at the beginning of each therapy session, rating their symptoms of PTSD on a four-point

Likert scale from 0 (not at all) to 3 (5 times per week/very much), with higher scores indicative of worse symptomatology. The PSS-SR has high internal consistency (α = .91), excellent interrater reliability for PTSD diagnosis (κ = .91) and overall severity (r = .97), and good one-month test-retest reliability (r=.74) (Foa et al., 1993).

Working Alliance Inventory-Patient- Short Form (WAI; Tracey & Kokotovic,

1989). The WAI evaluates the therapeutic alliance and comprises of 12 items scored on a

7-point Likert scale ranging from 1 (never) to 7 (always) with higher scores indicative of a stronger therapeutic alliance. Patients completed the WAI before sessions 2, 4, 6, 8, and

10 of treatment. The measure has demonstrated excellent reliability and internal consistency (Horvath & Greenberg, 1989).

Posttraumatic Cognitions Inventory (PTCI; Foa, Ehlers, Clark, Tolin, & Orsillo,

1999). The PTCI is a 36-item self-report measure that assesses negative trauma-related beliefs about the self, the world, and self-blame. Items are scored on a 7-point Likert scale ranging from 1 (totally disagree) to 7 (totally agree), with higher scores indicative of greater negative cognitions. The measure has good test-retest reliability and excellent

convergent validity, discriminant validity, and internal reliability (Foa et al., 1999).

Patients completed the PTCI before every treatment session.

Between Session Habituation. Between-session habituation was measured using the Subjective Units of Distress scale (SUDs; Wolpe & Lazarus, 1966). SUDs are self- rating of distress ranging from 0 (complete relaxation) to 100 (maximum distress).

During the imaginal exposure sessions, the therapist elicits state ratings of distress

(SUDs) from the patient every five minutes. Higher SUDs ratings are associated with physiological reactivity (Thyer, Papsdorf, Davis, & Vallecorsa, 1984), with measures of state anxiety (Kaplan, Smith, & Coons, 1995), and have been used extensively in the literature on habituation (Gallagher & Resick, 2012; van Minnen & Foa, 2006). The present study will utilize mean distress ratings from treatment sessions that included exposure (sessions 3 through 10).

Procedure

Participants, recruited from community referrals and flyers, were initially screened via a semi-structured phone interview and scheduled for an intake evaluation upon determination of potential eligibility. After informed consent was obtained, an independent evaluator blind to treatment condition conducted an intake evaluation collecting demographic and diagnostic information through structured interviews (e.g.,

PTSD Symptom Scale-Interview; Foa et al., 1993) to determine eligibility. At this randomization visit, participants additionally completed a battery of self-report measures including the PSS-SR and PTCI. Following this randomization visit, patients received 10 weekly sessions of PE or sertraline for their chronic PTSD. Upon the completion of

treatment, an independent evaluator blind to treatment condition conducted a post- treatment evaluation assessing PTSD symptoms.

Overview of treatment

Treatment consisted of 10 weeks of psychotherapy (PE) provided by master’s or doctoral level clinicians, or pharmacotherapy (sertraline) provided by board certified psychiatrists. All clinicians received a standardized clinical training prior to beginning the study, followed treatment manuals throughout the study, and received clinical supervision and consultation throughout the duration of the study.

Psychotherapy treatment. PE (Foa et al., 2007) consists of 10 weekly, 90-120 minute treatment sessions including psychoeducation about common reactions to trauma, breathing retraining, repeated in vivo exposure to situations the patient is avoiding due to trauma-related fear, prolonged (repeated) exposure to trauma memories (beginning in session 3), and emotional processing regarding thoughts and feelings related to the two exposure exercises. Homework is assigned in each session to promote and facilitate learning and processing, and is discussed at the following treatment session

Pharmacotherapy treatment. Sertraline treatment consisted of 10 weekly sessions of up to 30 minutes with a psychiatrist. Patients were started on a dosage of 25 mg/day and were increased up to 200mg/day if indicated and tolerated using a standard titration algorithm (Marshall, Beebe, Oldham, & Zaninelli, 2001). The final dosage for the sample in this study was 155 mg/day (SD = 59.0).

Treatment fidelity. All treatment sessions were audio recorded or videotaped and standard fidelity checklists were used by outside raters to assess protocol violations and adherence to required treatment components. Raters evaluated 10% of tapes and found no

protocol violations for PE or sertraline. PE therapists completed 90% of essential components and psychiatrists completed 96%. PE sessions were also rated for therapist competence on a 3-point Likert scale ranging from 1 (inadequate) to 3 (adequate or better). Overall, PE therapist competence was very good (M = 2.73, SD = .32).

Data Analytic Plan

To examine the relationship between symptom change and habituation, habituation and alliance, alliance and symptom change, and alliance and belief change, we utilized time-lagged, repeated-measures regressions (Rovine & Walls, 2006). We also ran the reverse model to examine the direction of effect (e.g., whether alliance mediates symptom change or vice versa). This statistical approach allows for an examination of potential temporal relationships between two variables, examining the strength of the relationship between a predictor at Time X, and a dependent variable at Time X + 1, while also controlling for the autocorrelation with that predictor at Time X, in line with theory (Curran & Bauer, 2011) and past methodological approaches (Cooper, Zoellner, et al., 2017; Strunk et al., 2012). Scores from the WAI (alliance), PSS-SR (PTSD symptoms), and PTCI (negative beliefs) included up to 5 time points: session 2 (Time 1), session 4 (Time 2), session 6 (Time 3), session 8 (Time 4), and session 10 (Time 5).

Habituation scores (mean session SUDs) included up to four time points: session 4 (Time

2), session 6 (Time 3), session 8 (Time 4), and session 10 (Time 5). For each variable, we created a set of dependent variables (Time 2 to Time 5) and a set of lagged predictors

(Time 1 to Time 4) combined in a single dataset. All models were tested in PROC Mixed with maximum likelihood in SAS 9.4. We conducted eight main sets of analyses including time as a covariate: (1) SUDs predicting PSS-SR, (2) PSS-SR predicting SUDs,

(3) WAI predicting SUDs, (4) SUDs predicting WAI, (5) WAI predicting PSS-SR, (6)

PSS-SR predicting WAI, (7) WAI predicting PTCI, and (8) PTCI predicting WAI.

Given the hierarchical nature of the treatment data, it is optimal to disentangle within-subject effects from between-subject effects (Curran & Bauer, 2011). Nested within patients (level 2), this study used repeated measurements (level 1) that compromise the independence assumption of linear models and can result in biased tests of model effects (Bauer, Preacher, & Gil, 2006). More specifically, a significant process- outcome relationship (e.g., alliance predicting next session PTSD symptoms) could simply be due to spurious effects of stable between-patient differences (e.g., effect of personality – a stable patient trait) if the between-patient and within-patient effects are not appropriately disaggregated. Thus, if researchers do not account for how much variance between-patient factors could be having on an outcome, incorrect conclusions might be made about causal processes; causal processes can only be inferred from the within-patient relationship between two variables. To address this limitation and further illustrate the importance of separating these effects, after running the initial time-lagged repeated measure regressions, we decomposed within-patient and between-patient variation for each measure by following procedures recommended by Curran and Bauer

(2011) and utilized by other researchers in examining process outcome relationships in cognitive therapy for depression (e.g., (Braun, Strunk, Sasso, & Cooper, 2015; Sasso,

Strunk, Braun, DeRubeis, & Brotman, 2016).

For each variable of interest (i.e., SUDs, WAI, PSS-SR, and PTCI), we conducted a series of separate ordinary least squares (OLS) regressions for each patient, in which we regressed each patient’s raw variable score on time (mean centered). We retained the

session-specific residuals from each patient’s model to obtain the within-patient scores at each time point, and the patient specific intercepts from these models to obtain the between-patient scores. Within-patient scores reflect the within-patient variability in a variable (predictor) and subsequent outcome that is not accounted for by any between- patient differences in stable characteristics. More specifically, the time-specific residuals from each regression model represent the deviation in each time-specific process score

(e.g., alliance) from the model-implied values of the process score for that session.

Between-patient scores reflect the between-patient variability reflecting between-patient characteristics such as personality or other stable traits. The patient specific intercepts obtained from the regression models reflect the value of the process variable (e.g., alliance). In a true causal process-outcome effect, the statistical relation between within- patient variability in the process variable (e.g., alliance) and outcome (e.g., PTSD symptoms) should be significant above and beyond any effect of between-patient variability (e.g., personality) in the process variable and outcome. By mean centering time (session), the intercepts reflect that value of the process variable at the mid-point of the sessions examined resulting in de-trended estimates thus avoiding violating the assumption of stationarity (i.e., the assumption of no change in the mean level of a repeated measures predictor across time; Falkenström et al., 2013). This method requires a minimum of three observations per patient for a non-saturated model so that the number of data points exceeds the number of parameters being estimated. As such, our patient sample size fluctuated due to some missingness. To illustrate for a given process variable

(e.g., WAI) where t = session and i = a given patient, this would be represented as:

!"#$% = '(% + '*$+,--./0$% + ,$% (1)

!"#$% is the session-specific patient score (i.e., the patient’s alliance score at a given session), '(% represents the model intercept (between-patient score or the variability attributed to stable patient characteristics), '*$is the slope of the WAI scores across the sessions, +,--./0$% is the measure of time, and ,$% is the session-specific residuals from the model (within-patient score or the variability not attributed to stable patient characteristics).

Next, we examined the within- and between-patient scores of the process variable

(,$% and '($) from equation 1 simultaneously as predictors of session-to-session symptom change in the same repeated measures regression model used with the aggregated scores

(i.e., the within- and between-patient WAI (alliance) scores were examined as predictors in one model, within- and between- patient habituation examined in another model, etc.).

This model can be represented by the following equation:

3+++4$5*% = 6( + 6*(3+++4$%) + 67('(% ) + 68(,$%) + 9$% (2)

3+++4$5*% represents a given patient’s PSS-SR score at time 2-5. 6( represents the intercept of PSS-SR scores at time 2-5. 6* reflects the effect of patients’ PSS-SR score at a given time (ti) on their PSS-SR score at the next time point (3+++4$5*% ). 67 represents the effect of between-patient variability on patients’ WAI score across time points (i.e., patients’ person-specific intercept from equation 1, or '(% ) on patients’ PSS-SR scores across time 2-5. 68 represents the effect of within-patient variability in patients’ WAI score at a given time point (i.e., patients’ session-specific residual from equation 1, or ,$%) on patients’ WAI score at the next time point. Lastly, 9$% represents the residual term or error from the model. We specified session (time point) as the repeated variable and patient as the subject. Except for the fact that these new models disaggregate within-

patient and between-patient variance in the process predictors, they are similar to what was first run with the time-lagged regressions and included time as a covariate (Rovine &

Walls, 2006). We used an unstructured covariance structure and specified maximum likelihood as the estimation method for covariance parameters and between-within as the method for computing the denominator degrees of freedom.

To test treatment (PE or sertraline) by process variable interactions where applicable, we examined our primary model (equation 2) with the following additional predictors: '(% * treatment and ,$%* treatment. If the interaction term was significant, we then ran the model separately within each treatment condition.

Results

Prior to examining process relations, we examined the mean and standard deviation of patients’ symptom and process variable scores at each session (Table 2). As described in the Data Analytic Plan, we obtained within-patient and between-patient scores from a series of regression models in which a given variable was regressed on time

(session) for each patient. Means and standard deviations for the within-patient and between-patient scores for each variable are provided in Table 3. Each patient only had one between-patient score (intercept from the model) per variable. For within-patient scores, each patient had five scores for each variable. Because residuals are parameterized to sum to zero (i.e., they represent the deviations between the model and the actual data), the mean for all within-patient scores was zero.

Additionally, we also examined the relationships among these scores. As shown in Table 4, correlations among the between-patient scores (shown below the diagonal) were significantly correlated for all but one of the between-patient variables. Compared

to between-patient scores, within-patient scores (shown above the diagonal) were substantially less related indicating that any significant relationship between these within- patient variables (i.e., in the lagged repeated measures regression models noted below) are less likely to be attributable to stable patient characteristics but rather be reflective of a causal relation between the process variable and outcome variable.

Habituation and PTSD Symptom Change

Results of the time-lagged models involving habituation (SUDs) and PTSD symptoms (PSS-SR) are presented in Table 5. For the model with PSS-SR as the dependent variable, the cross-lagged effect of habituation in predicting subsequent symptom reduction was significant (d = .35). For the model with habituation as the dependent variable, the cross-lagged effect of PTSD symptom reduction on habituation was also significant (d = .38), suggesting possible reciprocity between improvements in

PTSD symptoms and improvements in habituation.

Given the hierarchical nature of the data and the limitations of not disaggregating the between and within effects, we next examined the within- and between-patient scores

(,$% and '($ from equation 1) as predictors of session level symptom change in the same repeated measures regression model (as illustrated in equation 2). As can be seen in Table

6, results from the model with PSS-SR as the dependent variable revealed that between- patient SUDs scores significantly predicted next session symptom improvements (d =

.58) whereby within-patient SUD scores did not significantly predict next session symptom improvement (d = .27) suggesting that the majority of the variance in the aggregated model of SUDs predicting PSS-SR symptom reduction can be attributed to stable patient characteristics (i.e., between-patient SUDs) rather than a causal relation.

Similar results were found in the reverse model: between-patient PSS-SR scores significantly predicted next session SUDs scores (d =.43) whereas the within-patient

PSS-SR scores did not (d = .02). Thus, it appears that the effect of SUDs scores on PSS-

SR and the reverse—PSS-SR on SUDs scores—would be better understood as a relationship explained more by the effects of stable between-patient differences than by causal processes. The disaggregated results suggest that there may be a causal process in which SUDs scores predict PSS-SR reduction; however, as noted above, the relationship between within-patient SUDs on PSS-SR only trended toward significance (d = .27).

Alliance and Habituation Change

Results of the time-lagged models involving habituation (SUDs) and alliance

(WAI) are presented in Table 7. For the model with SUDs as the dependent variable, the cross-lagged effect of alliance improvements in predicting subsequent habituation was not significant (d = .15). For the model with alliance as the dependent variable, the cross- lagged effect of habituation on alliance improvements was moderate and significant (d =

.66), suggesting change in mean SUDs generally preceded improvements in alliance.

Once again, by examining the within- and between-patient scores as predictors in this model revealed that the majority of the variance in both the models could be attributed to the between-subject variability (Table 8). In the model predicting SUDs from time-lagged WAI, between-patient WAI improvements significantly predicted

SUDs scores (d = .51) but the effect of within-patient WAI on subsequent SUDs scores was negligible (d = .04). In the reverse model, between-patient SUDs significantly predicted WAI improvements (d = .63) but the effect of within-patient SUD on subsequent WAI scores was also negligible (d = .02). Thus, the significant effect (d =

.66) of time lagged SUDs on WAI improvements observed from the aggregated lagged repeated measures regression could be entirely explained by the between-patient variability and is thus not likely reflective of a causal process but rather stable patient characteristics.

Alliance and PTSD Symptom Change

Results of the time-lagged models involving WAI and PSS-SR are presented in

Table 9. For the model with alliance (WAI) as the dependent variable, the cross-lagged effect of PTSD symptom reduction (PSS-SR) in predicting subsequent WAI improvements was negligible (d = .03). For the model with PTSD symptom change as the dependent variable, the cross-lagged effect of WAI on PSS-SR was moderate and statistically significant (d = .31), suggesting that change in alliance generally preceded improvements in PTSD symptoms.

Re-running the analyses with the within- and between-patient scores as predictors did not substantially alter these findings (Table 10). In the model predicting PSS-SR from time-lagged WAI, between-patient WAI did not significantly predict improvements in

PSS-SR (d = .12) whereas within-patient WAI did significantly predict PSS-SR, albeit modestly (d = .24). In the reverse model, neither between-patient (d = .05) or within- patient (d = .09) PSS-SR significantly predicted WAI improvements, suggesting that change in alliance generally preceded improvements in PTSD symptoms above and beyond what could be attributed to stable patient traits.

To examine whether the relationship between alliance and PTSD symptom change differed by treatment modality, we tested Treatment * Predictor interaction in both models. In the model predicting change in PSS-SR, the Treatment * WAI interaction

was not a significant predictor of PTSD symptom change in either within-patient

(estimate = -.31, SE = .27, p = .25) or between-patient (estimate = -.15, SE = .09, p = .09)

WAI scores indicating that the relationship between alliance and PTSD symptoms did not differ by treatment modality.

Alliance and Belief Change

Results of the time-lagged models involving WAI and PTCI are presented in

Table 11. For the model with WAI as the dependent variable, the cross-lagged effect of

PTCI change in predicting subsequent WAI improvements was not significant and negligible (d = .03). For the model with belief change as the dependent variable, the cross-lagged effect of WAI on PTCI was statistically significant with a strong effect size

(d = .72), suggesting that change in alliance generally preceded improvements in PTSD symptoms in the aggregated model.

Upon examining the effects of within- and between-patient variability in scores

(Table 12), the majority of the variance in the model predicting PTCI from time-lagged

WAI could be explained by between-patient WAI scores (d = .33) whereas within-patient

WAI scores did not significantly predict subsequent PTCI scores (d = .10). The aggregated model predicting WAI from time-lagged PTCI scores was not significant; however, after disentangling the within- and between- effects, within-patient PTCI scores significantly predicted WAI scores (d = .25) whereas between-patient PTCI scores had a negligible effect (d = .04). Thus, the effect first noted in the aggregated lagged repeated measures regression predicting PTCI from time-lagged WAI could be explained by between-patient variability and is thus not reflective of a causal process. In contrast, our

disaggregated model revealed a significant effect of within-patient PTCI on WAI reflecting a potential causal effect of PTCI on subsequent WAI.

To examine whether the relationship between alliance and belief change differed by treatment modality, we tested Treatment X Predictor interactions in both models. In the model predicting change in WAI, neither the Treatment X within-patient PTCI interaction (estimate = .09, SE = .29, p = .76) or the Treatment * between-patient PTCI

(estimate = .03, SE = .06, p = .58) were significant indicating that there were no significant differences between treatments in the relationship between PTCI and subsequent WAI change.

In the model predicting change in PTCI, the Treatment X within-patient WAI interaction was significant (estimate = 1.38, SE = .64, p = .03) but the Treatment * between-patient WAI interaction was not significant (estimate = .36, SE = .21, p = .10).

Probing this interaction, we examined the relationship between preceding alliance on

PTCI in each treatment separately. In PE, within-patient WAI was significantly associated with subsequent PTCI change, Estimate = .72, SE = .29, p =.02, d = .31.

Interestingly, the effect of between-patient WAI was not significantly associated with subsequent PTCI change, Estimate = -.03, SE = .17, p =.87, d = .02. In the sertraline condition, within-patient WAI was also significantly associated with subsequent PTCI change, Estimate = -.71, SE = .36, p = .05, d = .38; however, between-patient WAI was more strongly predicted subsequent PTCI change in the sertraline condition, Estimate = -

.63, SE = .15, p < .0001, d = .84, suggesting that a larger proportion of the variance can be attributed to stable patient characteristics rather than a causal process in sertraline.

Discussion

Change in therapeutic alliance preceded PTSD symptom improvements in PE and sertraline above and beyond the contributing effects of between-patient variability - suggesting alliance may be a mechanism by which treatment leads to symptom reduction.

Change in alliance also predicted reduction in negative beliefs in PE and the reverse

(belief change preceding improvements in alliance) was found in the sertraline condition.

These results support the importance of the therapeutic relationships in both psychological and pharmacological treatments for PTSD. The relationships between habituation and symptom improvement and habituation and alliance were not meaningful predictors of outcome in the disaggregated analyses, contrasting past literature and longstanding hypotheses about habituation-related mechanisms of change in these treatments.

Prior to disentangling between and within effects, habituation predicted improvement in PTSD symptoms (d = .35) and improvement in PTSD symptoms also predicted habituation (d = .38) suggesting a reciprocal relationship between these variables over time. While past research has not examined the reverse model (i.e., PTSD symptoms predicting habituation), these results are in line with the majority of the literature showing associations with between session habituation and reductions in PTSD symptoms post treatment (de Kleine et al., 2015; van Minnen & Foa, 2006). However, upon disentangling the within- and between-subject effects, habituation no longer predicted symptom improvements or vice versa, evidencing that the relationships shown in the aggregated model could be attributed to stable patient traits rather than to causal processes. The between-patient variability in habituation and PTSD symptoms were

moderately correlated (r = .42) and thus it is likely that the effects between habituation and PTSD symptoms noted in the aggregated models were not only attributable to stable patient traits, but to shared variance as well. While these results are inconsistent with the longstanding assumption that habituation is a mechanism of change in PE, past studies have had methodological and analytic limitations including small sample size (e.g.,

Sripada & Rauch, 2015), inadequate attention to temporal sequencing (e.g., Nacash et al.,

2015), and inadequate attention to the role of between-patient variability on outcome

(e.g., e.g., Harned et al., 2015). Further, others have found habituation over the course of treatment may not be necessary for symptom improvement in exposure therapies (Bluett et al., 2014; Meuret, Seidel, Rosenfield, Hofmann, & Rosenfield, 2012), suggesting this construct may not be as critical for exposure success as originally hypothesized by EPT

(Foa & Kozak, 1986). One possible explanation is the concept of distress tolerance

(Craske et al., 2008). In a study examining the relationship between habituation and outcome, there was no difference in post-treatment PTSD diagnostic status between those who exhibited a reliable change in distress and those who did not show such a change

(Bluett et al., 2014). The authors interpreted this finding as arguing against habituation as a key mechanism necessary for improvement but theorized that perhaps a higher order mechanism, one that accounts for both habituation and distress tolerance, may be at play

(Bluett et al., 2014). Our findings support this literature by suggesting there may not be a causal relationship between habituation and symptom improvement in PE. It should be noted however that there is significant conceptual ambiguity around how to measure habituation with some studies measuring it by comparing peak SUDS (e.g., (Harned et

al., 2015) and others by comparing mean SUDs (e.g., (Gallagher & Resick, 2012). Our use of mean SUDs may not have adequately captured habituation.

Habituation meaningfully predicted improvements in alliance (d = .66) when within and between effects were not accounted for. This effect, however, was entirely accounted for by between-patient variability in habituation (d = .63); thus, the identified relationship observed in the simpler model is unlikely to be reflective of mechanistic processes. The reverse relationship (i.e., change in alliance predicting habituation) was not meaningfully robust in either the aggregated or disaggregated models. Thus, there is little evidence in our data to suggest habituation and alliance directly interact linearly with one another over the course of treatment.

In contrast to the results involving habituation, improvements in alliance meaningfully predicted PTSD symptom improvement, an effect not only seen in the aggregated model (d = .31) but that held when disentangling within and between effects

(within-patient WAI, d = .24) indicating that changes in alliance preceded changes in

PTSD symptoms, independent of stable patient characteristics such as a general ability to form a relationship. In the reverse model, change in PTSD symptoms did not meaningfully predict improvements in alliance in the aggregated model or disaggregated model. Further, there was no treatment interaction for either model, suggesting that therapeutic alliance is a mediator and possible mechanism of change in both PE and sertraline for PTSD.

While past lagged-design studies have found evidence for a reciprocal causal relationship between alliance and symptom change (Falkenström et al., 2013; Tasca &

Lampard, 2012), only one (Falkenström et al., 2013) disentangled the between-patient

and within-patient effects. However, effect sizes were not provided in that study, limiting an understanding of the strength of the observed relationships, and furthermore, data was from a primary care setting involved in the treatment of multiple disorders (e.g., anxiety, depression, grief) utilizing a range of therapies (e.g., psychodynamic, CBT, interpersonal;

Falkenström et al., 2013). Thus, our results suggest that in evidence-based treatment for

PTSD specifically, changes in the therapeutic alliance generally precedes and predicts improvements in next session PTSD symptoms above and beyond what can be attributed to stable patient traits, suggesting a probable causal process.

Though our results fit the linear models used in these analyses, it is also possible that change in alliance may be more complicated than what our study captured. As noted by others (Sasso et al., 2016), limited within-patient variability in raw variable scores could reduce power to detect within-patient process-outcome relations and thus reduce the strength of relationships. An examination of the raw mean data scores on our alliance measure across time (WAI range in mean scores of full sample: 65.02 – 70.96; see Table

1) reveals limited variability which could be attributed to a true lack of within-patient variability in the alliance, or, insensitivity of the measure to capture within-patient variability in alliance. Qualitatively, the mean alliance scores across time reveal a slight increase that seem to reach an asymptote; thus, perhaps alliance would be better examined using nonlinear models (A. M. Hayes, Laurenceau, Feldman, Strauss, &

Cardaciotto, 2007). Additionally, while not a focus of this study, it is also plausible that therapist effects might affect the relationship of alliance on outcome (Baldwin, Wampold,

& Imel, 2007). For example, when examining therapist effects in the delivery of cognitive processing therapy (CPT) for PTSD, therapist effects accounted for 12% of the

variability in post-treatment PTSD symptoms after controlling for baseline symptom severity (Laska, Smith, Wislocki, Minami, & Wampold, 2013). Moreover, ruptures and repairs in the therapeutic alliance are common (McLaughlin et al., 2014) and have been found to impact next-session symptom distress in outpatient CBT for anxiety and depression (Rubel, Zilcha-Mano, Feils-Klaus, & Lutz, 2018) thus a closer examination of such effects on alliance is warranted.

While improvements in the therapeutic alliance may precede symptom change, a more nuanced understanding of this construct will be important for identifying the specific facets of alliance that promote change such as therapeutic strategy, nonverbal expressions, agreed upon goals, etc. For example, Socratic questioning has been identified as a key therapeutic strategy in cognitive therapy (Beck, 1995) and in cognitive therapy for depression, greater levels of Socratic questioning has predicted next session symptom improvement (Braun et al., 2015). It could be that more Socratic questioning in therapeutic dialogue leads to a greater emotional bond and feelings of mutual agreement on goals and collaboration on tasks—key concepts thought to form the alliance (Bordin,

1979). By understanding how the alliance develops and if a specific component contributing to the alliance is more prominent in promoting symptom change, clinicians can work to modify their own approaches to treatment and training sites can help trainees learn how to enhance alliance to improve optimal outcomes of treatment.

Finally, improvement in alliance was a robust predictor of reduction in negative beliefs in our aggregated model (d = .72) but in the reverse model, belief change did not meaningfully predict alliance (d = .03). Disentangling the within- and between-patient effects revealed that while the overall model no longer predicted beliefs from alliance,

there was a significant treatment by within-patient-alliance interaction wherein improvements in alliance predicted belief change in PE but not in the sertraline condition.

In the reverse disaggregated model, within-patient belief change predicted stronger alliance across both treatments, suggesting some reciprocity between alliance and belief change. While little study has been focused on the relationship between alliance and belief change, change in negative cognitions have been consistently found to precede symptom change (Cooper, Zoellner, et al., 2017; Kleim et al., 2013; Kumpula et al.,

2017; Zalta et al., 2014) and are a central emphasis in theoretical models underlying the development and treatment of PTSD (Ehlers & Clark, 2000; Foa et al., 2006). Given the literature supporting the notion that belief change precedes PTSD symptom change (e.g.,

(Cooper, Zoellner, et al., 2017) and the results from the present study supporting alliance improvements preceding symptom change, it follows that the alliance improvements would also precede belief change. Belief change also in turn predicted improvements in alliance suggesting a possible bidirectional and interactive effect of these two constructs on one another. In light of the current findings of this study, it appears the therapeutic alliance might be a mechanism underlying symptom reduction, might be a mechanism underlying change in negative cognitions, or might be a construct that simply facilitates the mechanism of change in negative cognitions resulting in subsequent symptom reduction. Future work will need to untangle the likely complex interactions between alliance, negative cognitions, and PTSD symptoms.

Clinical Implications

Clinically, these results have several implications. The lack of a meaningful relationship between habituation and PTSD symptom reduction, and between habituation

and improvements in alliance should be reassuring to clinicians. In our data, habituation does not appear to be a prominent mediator of PE outcome and thus, may not be a necessary mechanism for symptom reduction. The practice of monitoring patient distress during imaginal exposure and, ideally, observing declines in distress, provides both patients and therapists with an “objective” measure of accomplishment and progress. In contrast, a lack of decline in distress may lead both patients and therapists to feel stuck and lead to a sense of failure. If habituation is not a necessary mechanism by which PE leads to treatment gains, therapists might consider shifting their focus away from encouraging and educating about distress ratings and reduction to focusing on the importance of corrective learning– be it by habituation or distress tolerance. Some hypothesize that the therapeutic focus on habituation perpetuates a “fear of fear” mindset that unintentionally implies anxiety itself is inherently bad (Abramowitz & Arch, 2014).

Recent research and advancement in the treatment of anxiety disorders has suggested that an inhibitory learning framework to exposure might provide a more nonthreatening and accurate approach to treatment wherein, rather than breaking with feared associations

(habituation), patients learn new non-threatening (inhibitory) associations. Furthermore, our results suggest that a lack of reduction in distress may not be indicative of a poor alliance or failure to engage a patient in treatment. As noted above, an approach to treatment that focuses on the importance of corrective learning rather than habituation per se might help clinicians allay fears regarding any presumed implications of habituation on alliance and on clinical outcome.

The relationship between improvements in the therapeutic alliance and outcome

(i.e., PTSD symptom change and belief change) suggests that alliance may both be a

mediator and potential mechanism by which treatment leads to symptom reduction. In

PE, clinicians must create a space to help patients confront the traumatic memory

(imaginal exposure) and be receptive to re-engaging with trauma-related stimuli and situations (in-vivo exposure) (Foa et al., 2007). It is plausible that the formation of the therapeutic alliance facilitates patients’ receptivity to engaging with treatment and homework completion—necessary actions in the course of helping patients modify their negative cognitions and experience symptom reduction. It may be that a stronger therapeutic alliance leads to better homework compliance and adherence in therapy, thereby promoting treatment retention and better treatment outcomes (Cooper, Kline, et al., 2017; Cooper et al., 2016). Of note, alliance predicted next session PTSD symptom change in both the psychotherapy treatment group and the pharmacotherapy group. While alliance with prescribing providers has traditionally not been studied in pharmacological treatments, evidence suggests the alliance may play an important role in medication adherence. In a large effectiveness study investigating the course and treatment of bipolar disorder, patients’ perceptions of collaboration, empathy, and accessibility with their provider was significantly associated with medication adherence (Sylvia et al., 2013).

Additional research in schizophrenia has found similar results supporting the effect of stronger alliance on medication adherence in pharmacological treatments (Tessier et al.,

2017). Thus, it could be that some other third variable such as adherence mediates the relationship between alliance and outcome. These findings are important for failure to take medication is the primary form of nonadherence in pharmacotherapy that cannot be compensated for by techniques commonly used in psychotherapy (e.g., “You did not do your homework so let us do it together here in session”). Finding ways to increase

adherence will lead to better treatment outcomes and reduce the public health consequences of treatment failure and premature dropout (Barrett et al., 2008).

Regardless of the specific processes by which alliance leads to symptom reduction, our results support the notion that the therapeutic alliance is important to the success of evidence-based treatments for PTSD and should be attended to by the therapist.

Research Implications

As our results show, attending to (or not attending to) the between-patient and within-patient variability in scores can alter findings and have a substantial impact on conclusions drawn from data. After disaggregating all of the variables examined in this paper, only three significant findings indicative of causal processes emerged: improvements in alliance predicted next session PTSD symptom reduction in both treatments, improvements in alliance predicted next session negative belief change in PE, and belief change predicted next session alliance improvements in both treatments.

Notably, the relationships between habituation and PTSD symptom improvements and habituation and alliance improvements—while at times significant with aggregated models—were insignificant, illustrating the importance of robust statistical analytic models in untangling the nuances of mechanism research. Had we not disaggregated within-patient and between-patient variability, we would have failed to recognize that the relation of these variables and outcome in our sample was largely due to mean differences across patients rather than causal processes. Given the use of temporal sequencing and accounting for any between-patient effects coupled with the examination of multiple putative mediators within the same dataset, these results suggest that habituation may not be a necessary process for symptom reduction and that the

therapeutic alliance may be mediator and potential mechanism responsible for change in negative beliefs as well as change in PTSD symptoms.

Given the strength in variability of between-patient effects, additional research will need to examine the extent to which patient characteristics play a role in treatment outcome and perhaps moderate various putative mechanisms of change. It could be that patient characteristics have a differential effect on different mechanisms such that treatments and active ingredient techniques (e.g., in vivo homework, imaginal) might be able to be matched with certain kinds of patients and augmented to meet the needs of individual patients. In other words, while PE might work on a diverse group of patients, it may not work in the same way for every patient (Cooper, Clifton, et al., 2017). By uncovering the specifics underlying change and symptom reduction, clinicians will be able to tailor treatments to meet the needs of individual patients thus personalizing care, and theoretically, advancing the success of such interventions.

Limitations

Key strengths of this study include a robust analytic strategy that disentangled between-patient and within-patient effects, stringent study sample selection criteria that ensured all patients included achieved an adequate “dose” of treatment thereby resulting in little missing data, and a large and diverse sample of male and female patients with heterogeneous traumas. However, a number of limitations should be noted. While we were able to satisfy the majority of criteria for establishing mediators as a mechanism of change (e.g., temporal precedence, within-patient effects), we did not have an experimental manipulation of the variables of interest and thus cannot definitively make causal conclusions about the relationships between the variables studied and outcome

(Kazdin, 2007). Nonetheless, our analysis of within-patient variation in putative mediators of PTSD treatment was able to rule out the influence of any stable third variables—such as stable patient characteristics—that might be contributing to the effect of that variable on outcome. And indeed, we found that in most cases, the between- patient effects did account for the relationship between the variable of interest and outcome thus illustrating the importance of disaggregated analyses when examining potential causal relationships in longitudinal repeated measure designs.

Second, due to the timing of our data collection with various measures, our analyses utilized data from sessions 2, 4, 6, 8, and 10 of treatment and thus we did not examine change after each session but rather change after every two sessions. However, with five data points, our models remained well-suited for examining disaggregated change over time. Third, given that PE is firmly grounded in cognitive behavioral therapy, it is likely that the results from this study will generalize to other cognitive behavioral treatments; however, it may be that our results are more limited to PE for

PTSD and that process-outcome relations vary across psychotherapies and across diagnostic presentations. Considering the limited research on process-variable relations in

SSRI treatments, our results may not generalize to other pharmacotherapies; however past research has supported the importance of the alliance on medication adherence.

Fourth, the statistical approach used in this paper has not been evaluated with simulated datasets to evaluate minimum sample sizes for adequate power and thus the precision of our estimates (i.e., residuals, intercepts, and effect sizes detected) may be questioned. Of note, our study included unequal samples in PE and sertraline conditions which could have additionally affected power. Though future work will need to examine

the power of such models and effects of unequal samples, with 144 patients, our study was likely adequately powered. Finally, our analyses did not examine the effect of all significant predictors in a combined model simultaneously. Thus, some of the relations obtained between variables may be partly accounted for by another variable (i.e., overlapping variance). Thus, future work with larger sample sizes will need to examine all predictors/putative mediators of symptom change simultaneously.

To our knowledge, this is the first paper to examine the relationship between multiple putative mediators of PTSD treatment with outcome variables (e.g., PTSD symptoms) and with one another to illustrate the interactive nature of these constructs.

Furthermore, this is the first effort to disentangle the within-patient variability in scores from the between-patient variability in scores to examine the extent to which a proposed process (e.g., alliance) plays a causal role in predicting a next-session dependent variable

(e.g., PTSD symptom reduction). By examining both aggregated and disaggregated models, this paper provides a tangible illustration as to how effects can be masked (i.e.,

Type II error) or falsely identified (i.e., Type I error) when attention is not directed to parsing out the variation attributed to stable patient traits (between-subjects effect) versus the variation that can be attributed to a true causal relationship (within-subjects effect).

Conclusion

In sum, our findings imply that, regardless of other active treatment ingredients, how connected patients feel with their providers affects how well they do during PTSD treatment. While these results may suggest a special emphasis on developing and maintaining a strong alliance with patients in treatment for PTSD, our parent study was focused on the effects of PE and sertraline and not on specific attention to the alliance.

Thus, we hypothesize that the treatment components of PE (e.g., psychoeducation, breathing retraining, guiding patients through imaginal exposure, emotional processing) directly contribute to building a strong alliance by normalizing symptoms, providing coping strategies, and collaborating with the patient to help them learn to tolerate their distress and find new ways of thinking about their trauma, thus facilitating buy-in to PE and the formation of a strong alliance. Similarly, the alliance may facilitate buy-in and adherence to biological interventions such as sertraline.

Our results have clear and meaningful clinical implications. In both PE and sertraline conditions, the alliance is an important process leading to symptom reduction in this sample. It is possible that the alliance may play a critical role in fostering a supportive environment for exposures in PE and encouraging adherence to homework

(PE) and medication compliance (sertraline). While we cannot definitively say the alliance in of itself is responsible for symptom reduction, it is likely it is a key facilitator in setting the stage for treatment success. Thus, it is critical for both psychotherapy providers and psychiatrists to attend to the alliance and repair any therapeutic-ruptures during treatment (McLaughlin et al., 2014). As illustrated by Kazdin (2007), in order to definitively claim the alliance as a mechanistic construct, future research must a) replicate our findings using disaggregated data and temporal models, b) show through direct manipulation experimental evidence supporting the relationship between the alliance and outcome, c) show a gradient in which stronger alliances correspond to greater levels of symptom reduction, and d) rule out other plausible reasons for the relationship (e.g., therapist effects). Additional research should examine potential mediators and moderators of the alliance-outcome relationship. Perhaps mechanisms of

change in PTSD treatment play a differential role fostering treatment gains for different people. By further studying the role of alliance in treatment outcome, we will be able to best optimize and personalize treatments to specific patients.

Table 1.

Demographic Characteristics in Full Sample and by Treatment Type

Full Sample PE Sertraline

(n = 144) (n = 86) (n = 58) Characteristics n % n % n % Female 112 77.8 71 82.6 41 70.7 Caucasian 102 70.8 60 69.8 42 72.4 Not college educated 97 67.4 52 60.5 45 77.6 Primary Trauma Physical/sexual assault as adult 78 54.1 45 52.3 33 56.9 Physical/sexual assault as child 32 21.7 22 25.6 10 17.2 Accident or natural disaster 22 15.3 13 15.1 9 15.5 Combat 4 2.8 2 2.3 2 3.4 Death of loved one 8 5.6 4 4.7 4 6.9 M SD M SD M SD Age 37.7 11.8 36.2 11.5 40.0 11.8 Time since target trauma 12.4 13.0 12.5 12.5 12.4 13.7 Note. PE = Prolonged Exposure

Table 2

Means and Standard Deviations of Patients’ Raw Process Variable and PSS-SR Scores at Each Session for Patients in Both Conditions, Combined, and in Each Condition

Full Sample Variable Range Session 2 Session 4 Session 6 Session 8 Session 10 WAI 12 - 84 65.02 66.77 69.28 70.90 70.96 (12.71) (12.33) (12.19) (11.21) (12.04) PSS-SR 0 - 51 30.11 27.22 22.35 19.07 14.86 (10.11) (10.94) (10.81) (12.03) (11.89) PTCI 36 - 139.72 131.70 118.07 107.35 98.58 252 (39.72) (42.88) (43.46) (41.76) (40.19)

Prolonged Exposure SUDs 0 - 100 - 59.61 53.04 50.43 41.78 (19.22) (21.30) (21.20) (21.07) WAI 12 - 84 67.08 69.10 71.13 70.57 72.68 (10.25) (10.29) (10.74) (11.65) (9.74) PSS-SR 0 - 51 30.59 29.24 22.21 19.42 13.82 (10.15) (9.28) (10.36) (11.17) (10.45) PTCI 36 - 140.65 133.86 119.16 106.17 92.15 252 (38.30) (41.97) (41.81) (40.82) (35.31)

Sertraline WAI 12 - 84 61.42 62.78 65.61 71.54 68.50 (15.60) (14.46) (14.09) (10.42) (14.47) PSS-SR 0 - 51 29.27 23.64 22.63 18.36 16.28 (10.10) (12.71) (11.80) (13.70) (13.62) PTCI 36 - 138.01 127.71 115.83 109.69 107.72 252 (42.58) (44.70) (47.17) (44.03) (45.04) Note. Means are reported outside of the parentheses, standard deviations reported in parentheses. SUDs = Average Subjective Units of Distress; WAI = Working Alliance Inventory; PSS-SR = Posttraumatic Stress Disorder Symptom Scale-Self Report; PTCI = Posttraumatic Cognitions Inventory

Table 3

Means and Standard Deviations of the Within- and Between-Patient Scores in Both Conditions Combined and in Each Condition

PE & Sertraline PE Sertraline Between Within Between Within Between Within SUDs - - 49.36 0 - - (19.02) (10.92) WAI 68.58 0 70.63 0 65.42 0 (11.69) (3.60) (9.87) (3.18) (13.47) (4.25) PSS- 22.35 0 22.90 0 21.45 0 SR (9.35) (3.94) (8.46) (4.00) (10.56) (3.84) PTCI 133.66 0 135.34 0 131.16 0 (38.26) (8.61) (36.53) (8.58) (40.63) (8.67) Note. Means are reported outside of the parentheses, standard deviations reported in parentheses. SUDs = Average Subjective Units of Distress; WAI = Working Alliance Inventory; PSS-SR = Posttraumatic Stress Disorder Symptom Scale-Self Report; PTCI = Posttraumatic Cognitions Inventory

61 Table 4

Correlations among Between-Patient (below diagonal) and Within-Patient (above diagonal) process variables and PTSD symptoms in Both Conditions Combined and in Each Condition Separately

PE & Sertraline PE Sertraline 1 2 3 4 1 2 3 4 1 2 3 4 1. SUDs ------.07 .07 .09 - - - - 2. WAI - - -.09 * .07 .035 - .00 .11* - - -.21** -.00 3. PSS-SR - -.16*** - .04 .42*** -.17*** - .06 - -.20*** - -.01 4. PTCI - -.09 * .49 *** - .26*** .047 .52*** - - -.28*** .45*** - Note. SUDs = Average Subjective Units of Distress; WAI = Working Alliance Inventory; PSS-SR = Posttraumatic Stress Disorder Symptom Scale-Self Report; PTCI = Posttraumatic Cognitions Inventory; PE = Prolonged Exposure *Correlation is significant at the .05 level (2-tailed) ** Correlation is significant at the .01 level (2-tailed) *** Correlation is significant at the .001 level (2-tailed)

Table 5

Time Lagged Multilevel Regressions of Habituation (Mean Subjective Units of Distress; SUDs) and Posttraumatic Stress Disorder (PTSD) Symptoms (PTSD Symptom Scale-Self Report; PSS-SR)

Variable Estimate SE t p d Predicting PSS-SR from time-lagged SUDs Intercept 3.44 3.15 1.09 .28 .17 Time -1.45 .78 -1.85 .07 .28 PSS-SR .62 .06 10.84 <.0001 1.65 autocorrelation Lagged SUDs .06 .03 2.27 .03 .35 Predicting SUDs from time-lagged PSS-SR Intercept 12.69 6.33 2.01 .05 .31 Time -2.04 1.49 -1.37 .17 .21 SUDs autocorrelation .57 .05 10.52 <.0001 1.60 Lagged PSS-SR .30 .12 2.48 .02 .38 Note. SUDs = Average Subjective Units of Distress; PSS-SR = Posttraumatic Stress Disorder Symptom Scale-Self Report; d = Cohen’s d, where d = t*Ö(2/n) N = 86

Table 6

Time Lagged Multilevel Regressions of Habituation (Mean Subjective Units of Distress; SUD) and Posttraumatic Stress Disorder (PTSD) Symptoms (PTSD Symptom Scale-Self Report; PSS-SR) in the Aggregated Model and Disaggregated Model (Within and Between Effects)

Variable Estimate SE t p d Predicting PSS-SR from time-lagged SUD Aggregated SUD .06 .03 2.27 .03 .35 Within SUD -.09 .05 -1.75 .08 .27 Between SUD .15 .05 3.73 <.001 .58 Predicting SUD from time-lagged PSS-SR Aggregated PSS-SR .30 .12 2.48 .02 .38 Within PSS-SR -.05 .31 -.15 .88 .02 Between PSS-SR .53 .19 2.79 .006 .43 Note. SUDs = Average Subjective Units of Distress; PSS-SR = Posttraumatic Stress Disorder Symptom Scale-Self Report; d = Cohen’s d, where d = t*Ö(2/n) N = 86 for Aggregated, N = 83 for Within and Between

Table 7

Time Lagged Multilevel Regressions of Habituation (mean Subjective Units of Distress; SUD) and Alliance (Working Alliance Inventory; WAI)

Variable Estimate SE t p d Predicting SUD from time-lagged WAI Intercept 22.33 6.12 3.65 <.001 .56 Time -3.39 1.37 -2.47 .02 .38 SUD autocorrelation .66 .05 13.06 <.0001 1.99 Lagged WAI -.05 .05 -.96 .34 .15 Predicting WAI from time-lagged SUD Intercept 27.91 7.78 3.59 <.001 .55 Time .09 1.76 .05 .96 .01 WAI autocorrelation .29 .07 4.21 <.0001 .64 Lagged SUD .29 .07 4.33 <.0001 .66 Note. SUDs = Average Subjective Units of Distress; WAI = Working Alliance Inventory; d = Cohen’s d, where d = t*Ö(2/n) N = 86

Table 8

Time Lagged Multilevel Regressions of Habituation (Mean Subjective Units of Distress; SUDs) and Alliance (Working Alliance Inventory; WAI) in the Aggregated Model and Disaggregated Model (Within and Between Effects)

Variable Estimate SE t p d Predicting SUD from time-lagged WAI Aggregated WAI -.05 .05 -.96 .34 .15 Within WAI .12 .39 .31 .76 .04 Between WAI -.47 .14 -3.29 .002 .51 Predicting WAI from time-lagged SUD Aggregated SUD .29 .07 4.33 <.0001 .66 Within SUD .02 .11 .16 .87 .02 Between SUD .45 .12 4.12 <.001 .63 Note. SUDs = Average Subjective Units of Distress; WAI = Working Alliance Inventory; d = Cohen’s d, where d = t*Ö(2/n) N = 86 for Aggregated, N = 83 for Within and Between

Table 9

Time Lagged Multilevel Regressions of Alliance (Working Alliance Inventory; WAI) and Posttraumatic Stress Disorder (PTSD) Symptoms (PTSD Symptom Scale-Self Report; PSS-SR

Variable Estimate SE t p d Predicting PSS-SR from time-lagged WAI Intercept 9.64 1.61 5.99 <.0001 .71 Time -1.37 .41 -3.37 <.001 .40 PSS-SR autocorrelation .64 .04 16.81 <.0001 1.98 Lagged WAI -.05 .02 -2.63 .009 .31 Predicting WAI from time- lagged PSS-SR Intercept 33.27 3.79 8.77 <.0001 1.03 Time -.06 1.03 -.05 .96 .01 WAI autocorrelation .41 .05 9.03 <.0001 1.06 Lagged PSS-SR .02 .09 .20 .84 .02 Note. WAI = Working Alliance Inventory; PSS-SR = Posttraumatic Stress Disorder Symptom Scale-Self Report; d = Cohen’s d, where d = t*Ö(2/n) N = 144

Table 10

Time Lagged Multilevel Regressions of Alliance (Working Alliance Inventory; WAI) and Posttraumatic Stress Disorder (PTSD) Symptoms (PTSD Symptom Scale-Self Report; PSS-SR) in the Aggregated Model and Disaggregated Model (Within and Between Effects)

Variable Estimate SE t p d Predicting PSS-SR from time-lagged WAI Aggregated WAI -.05 .02 -2.63 .009 .31 Within WAI .27 .13 2.03 .04 .24 Between WAI -.05 .05 -1.03 .30 .12 Predicting WAI from time- lagged PSS-SR Aggregated PSS-SR .02 .09 .20 .84 .02 Within PSS-SR .23 .29 .79 .43 .09 Between PSS-SR -.05 .12 -.43 .66 .05 Note. WAI = Working Alliance Inventory; PSS-SR = Posttraumatic Stress Disorder Symptom Scale-Self Report; d = Cohen’s d, where d = t*Ö(2/n) N = 144 for aggregated; N = 140 for Within and Between

Table 11

Time Lagged Multilevel Regressions of Alliance (Working Alliance Inventory; WAI) and Beliefs (Post-Traumatic Cognitions Inventory; PTCI)

Variable Estimate SE t p d Predicting PTCI from time-lagged WAI Intercept 7.78 4.42 1.76 .08 .21 Time -2.18 .87 -2.51 .01 .30 PTCI autocorrelation .83 .02 39.87 <.0001 4.70 Lagged WAI .24 .04 6.07 <.0001 .72 Predicting WAI from time-lagged PTCI Intercept 33.00 4.47 7.38 <.0001 .87 Time -.11 .96 -.12 .91 .01 WAI autocorrelation .41 .04 10.43 <.0001 1.23 Lagged PTCI .00 .02 .22 .83 .03 Note. WAI = Working Alliance Inventory; PTCI = Posttraumatic Cognitions Inventory; d = Cohen’s d, where d = t*Ö(2/n) N = 144

Table 12

Time Lagged Multilevel Regressions of Alliance (Working Alliance Inventory; WAI) and Beliefs (Post-Traumatic Cognitions Inventory; PTCI) in the Aggregated Model and Disaggregated Model (Within and Between Effects)

Variable Estimate SE t p d Predicting PTCI from time- lagged WAI Aggregated WAI .24 .04 6.07 <.0001 .72 Within WAI -.29 .33 -.88 .38 .10 Between WAI -.28 .10 -2.82 .006 .33 Predicting WAI from time- lagged PTCI Aggregated PTCI .00 .02 .22 .83 .03 Within PTCI -.29 .14 -2.02 .05 .25 Between PTCI -.01 .03 -.31 .76 .04 Note. WAI = Working Alliance Inventory; PTCI = Posttraumatic Cognitions Inventory; d = Cohen’s d, where d = t*Ö(2/n) N = 144 for aggregated; N = 135 for Within and Between

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