An Introduction to Clinical Trials: Type of Studies Design Issues • Non-experimental (Observational) – Case report Edgar R Miller III PhD, MD – Case series Welch Center for Prevention, Epidemiology and Clinical Research – Cross-sectional (survey) Johns Hopkins University – Case-control School of Medicine and Bloomberg School of – Prospective, observational (cohort) Public Health • Experimental – Randomized, clinical trial (RCT)
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Study designs Advantages of Clinical Trials
• Observational studies: • Often provides the strongest evidence in – Observe both exposures and outcomes support of cause-effect relationships • Experimental studies (clinical trials) – Assign exposures • Basis for clinical and public health policy – Observe outcomes • Minimize/eliminate bias and confounding
3 4 Randomized Clinical Trial Comparison of Study Designs
Target Population Type of Study Design
Cross- Case- Study Population Dimension Sectional Control Cohort RCT Estimate A - B - Prevalence RANDOMIZED Estimate - - A B Incidence Prove C B- B+ A Standard Treatment New Treatment Causality Generalizability A B+ B+ B
Disease Disease Feasability A A B C
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Core Elements of a Clinical Trial The Research Question
• Research Question •Data • Critical in the design of a trial • Hypotheses • Analytical Issues • Types of questions: • Core Design • Interpretation of – Assessing efficacy of an intervention • Study Participants Results – Assessing the effectiveness of an intervention • Recruitment • Allocation • Masking (Blinding) • Treatment Groups
7 8 Types of Hypotheses Types of Hypotheses
• Comparative Trial (a.k.a. Superiority Trial) • Equivalence (non-inferiority trial) – Objective: to demonstrate that a new therapy – Objective: to demonstrate that a new therapy (n) is superior to standard therapy (s) in terms (n) is no worse than standard therapy (s) in of incident outcome (I) terms of incident outcome (I)
HO: In = Is HO: In > Is
HA: In < Is (one tailed) or HA: In ≠ Is (two tailed) at HA: In = Is at some ∆, the maximum tolerable some minimally detectable ∆ judged to have clinical difference considered to be clinically acceptable significance
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Basic Types of Design Parallel Study Design (PREMIER)
A Parallel Randomization
B ADVICE ONLY EST A A Cross-Over EST + DASH
BB Primary End of Outcomes Intervention = Data Visit 11 (6 months) (18 months) Cross-Over Study Design
4 Control Diet Fruits-and-vegetables Diet DASH (OmniHeart) 2
0 Randomization Washout Washout to 1 of 6 -2 Period Period sequences 2–4 wk 2-4 wk -4
-6 * Screening/ Run-In Period 1 Period 2 Period 3 -8 Baseline 6 days 6 weeks 6 weeks 6 weeks -10 ** Data: -12 Baseline1234567 and 8 Participants Ate Their Own Food Intervention Week Conlin et al., Am J Hypertens, 2002 Participants Ate Study Food
Blood Pressure Results Mixed Study Design (DASH-Sodium)
(mmHg) Randomized Sequence Randomization Mean Change from Baseline in to Diet Each Diet Lower Intermediate Higher Sodium Sodium Sodium Systolic BP Baseline CARB PROT UNSAT Usual Diet Usual Diet DASH Diet All 131.2 -8.2 -9.5 -9.3 Lower Intermediate Higher Sodium Sodium Sodium HTN Only 146.5 -12.9 -16.1 -15.8
Run-in (11-14 days) Intervention (three 30-d periods in random order) PreHTN Only 127.5 -7.0 -8.0 -7.7
Diastolic BP 77.0 -4.1 -5.2 -4.8
Appel et al. 2005 16 Effect of Increased Sodium Intake on Systolic Blood Pressure in Two Diets: Results of the DASH- Factorial Design Sodium Trial* • Type of trial in which individuals are randomized to two or more therapies (example: Physician’s Health 135 American Diet Study: tested aspirin (ASA) and β-carotene +2.1
+6.7 +4.6 No β-carotene β-carotene Systolic 130 p<.0001 Neither β-carotene Blood No 10,000 ASA only Pressure +3.0 125 P<.0001 +1.3 ASA only Both 10,000 +1.7 DASH Diet ASA 120 65 100 140 10,000 10,000 20,00018 *Sacks et al, 2001 Approximate Daily Sodium Intake (mmol/day)
The African American Study of Kidney Disease AASK Research Questions and Hypertension (AASK) Among African-Americans with early evidence of hypertension-related kidney disease:
• Does aggressive blood pressure control to a target blood pressure below current recommendations retard the progression of kidney disease?
• Do specific classes of anti-hypertensive medications retard the progression of kidney disease? Treatment Assignments Design of AASK (2:2:1 ratio of drug assignment) 3 X 2 Factorial Design
• Randomized, active controlled trial with a 2 x 3 factorial design Metoprolol* Ramipril Amlodipine
• Participants: 1,094 African-Americans MAP <92 20% 20% 10% with hypertension-related renal insufficiency MAP 102-107 20% 20% 10%
• Planned follow-up of 2.5 to 5 years N 441 436 217
MAP = Mean Arterial Pressure; * = referent group
Mean Arterial Pressure During Follow-up Composite Clinical Outcome 130 Declining GFR Event, ESRD or Death 40 Lower BP Goal (Achieved: 128/78) Lower BP (Achieved: 128/78) 120 Usual BP Goal (Achieved: 141/85) 35 Usual BP (Achieved: 141/85) 30 Low vs. Usual: RR=2%, (p=0.85) 110 25 20 100 15 MAP (mm Hg) MAP % with Events % with 10 90 5 0 80 0 6 12 18 24 30 36 42 48 54 60 0 4 12 20 28 36 44 52 60 Follow-Up Time (Months) Follow-up Month 23 24 RR=Risk Reduction, adjusted for baseline covariates Main Clinical Composite Outcome Declining GFR Event, ESRD, or Death 40 Amlodipine Ramipril 35 Metoprolol 30 Ramipril vs. Metoprolol 25 RR = 22%, p = 0.042 20 15 Metoprolol vs. Amlodipine: 10 RR= 20%, p=0.17 %withEvents 5 Ramipril vs. Amlodipine: RR= 38%, p=0.004 0 0 6 12 18 24 30 36 42 48 54 60 Follow-up Month RR = Risk Reduction25 RR = Risk Reduction, Adjusting for Baseline Covariates
Study Participants Study Participants: Example
Target Accessible Population • Target Population -> Healthy Elderly Population
• Accessible Population -> Retired Teachers
Study Samples • Study Sample -> Volunteer Teachers who respond to mass mailing
27 28 Study Participants Enrollment Criteria • Inclusion Criteria • Ideal ‘Accessible’ Population – characteristics of accessible population – high risk for disease • Exclusion Criteria – candidates for treatment – considerations related to: – representative of target population • adherence to therapy – feasibility considerations • follow-up • recruitment • safety • follow-up •ethics • high quality data
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Common Recruitment Strategies Comments on Recruitment
• General mailings • Screenings – Licensed drivers – Worksite • Recruitment begins with design – Voters – Community • Response rate is always lower than – Employee paychecks • Physician Referral • Targeted mailings • Medical Record Review expected – HMO enrollees • Internet / WWW • Required resources are more than – AARP members – Clinical trial registries • Mass media – Banner ads expected – Radio – Social networks – TV ads • Dedicated personnel are necessary – Newspapers – Posters/flyers
31 32 Recruitment “Funnel” More Comments on Recruitment (Example: VITAL Pilot Study) • Recruitment period is often longer than expected 4,774 Mailed Invitations • Implement several strategies to identify best 43% 2,034 Questionnaires Returned source 38% • Prepare back-up strategies 765 Interested After Initial Mailing • Monitor recruitment 41% –Early 323 Randomizable after Second Mailing (7% cumulative) – Often 297 Randomized – Locally
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Allocation Why randomize?
• Random – stratified • Two critical reasons: – blocked – to eliminate selection BIAS • Non-Random – to reduce/avoid CONFOUNDING from known and, more importantly, unknown confounders – haphazard – systematic
35 36 Masking (Blinding) Masking (Blinding)
• Single Blind Single Double Triple – Observers (persons who collect outcome Masked Masked Masked variable) do not know treatment assignment Outcome XX X • Double Blind Assessor(s) – Study participants AND observers do not know Participant X X treatment assignments • Triple Blind Data X – Data interpreters, study participants, and Interpreter
observers do not know treatment assignments37 38
Problems with selecting Selection of Groups active treatment group • Active Treatment Group • Many Candidate treatments – observation studies, animal models, or • Comparison Group theoretically based – Placebo (no active therapy) – Usual care (referral back to personal MD) • Strong evidence rarely exists to guide – Active control group (provision of standard selection of intervention therapy)
39 • Dose/intensity are uncertain 40 Problems with standard of Comparison Group care approach
• Placebo – used in setting of: • Efficacy of ‘Usual care’ often not tested – No standard therapy OR – Standard therapy but risk of not providing it • Variations in standard of care are common: is minimal – across providers – between experts and providers • Usual care OR active control – common – secular trends occur
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Data Outcome Variables
• Baseline data • Principal outcome – Determine eligibility – most important variable after – Describe study participants randomization code – Define subgroups – specified in hypothesis – Address confounding – determinant of sample size • Measures of Adherence • Secondary Outcomes • Outcome Variables – relevant to research question
43 44 Desirable Features of Surrogate Outcomes Outcome Variable • Definition: a laboratory measurement • clinically relevant or physical sign used as a substitute • easy to measure for a clinically meaningful outcome • little measurement error –random error – leads to imprecision • Types: physiologic variable, clinical –systematic error – leads to bias risk factor, or sub-clinical disease • masked (blinded) ascertainment 45 46
Advantages of Surrogate Advantages of Surrogate Outcomes Outcomes (continued) • Surrogate outcomes typically increase • Enhanced power means statistical power compared to clinical outcomes – shorter duration of follow-up and/or reduced sample size – Surrogate outcomes • often continuous – less cost • measured repeatedly • Less contamination by competing – Clinical outcomes comorbidities if the study duration is short • often categorical • Useful in studies of mechanisms
• surveillance till outcome occurs 47 48 Surrogate and clinical outcomes: Surrogate and clinical outcomes: a continuum an example
Antecedent Established Morbid Cause- Total Weight Blood Angina MI CVD Total of the Risk Risk Factor Events Specific Mortality Pressure Mortality Mortality Factor Mortality
Relationship between Change in Relationship between Surrogate Surrogate Outcome and Change and Clinical Outcomes in Clinical Outcomes
Surrogate Outcome Change in Surrogate Outcome Clinical and Surrogate Outcomes: Cardiovascular Clinical Surrogate Stroke Ultrasound measurement of intimal medial thickness of the carotid artery Blood pressure Myocardial Quantitative coronary angiography infarction Electron beam computerzied tomography Sudden Ventricular arrhythmia death Heart failure Ejection fraction
Disadvantages of Surrogate Outcomes
Weaknesses • Measurement of surrogate outcomes can involve complex, technical procedures – procedures sometimes new (therefore, longitudinal data is scant) – procedures become obsolete – many technical and analytic issues, often unapparent Disadvantages of Surrogate Outcomes (continued) • Missing values are commonplace Models for success and • Missing values result from loss to follow-up and failure of surrogate poor quality of data • Potential for bias outcomes* – missing values occur in the sickest people, sometimes because of the clinical outcome of interest *Fleming TR, DeMets DL. Surrogate End – informative censoring, that is, loss of follow- Points in Clinical Trials: Are we being mislead? up data potentially related to treatment Ann Int Med 1996;125:605-613. assignment
Model for potential success: Surrogate Model for potential success: Surrogate outcome in the casual pathway outcome in the casual pathway
Intervention Diuretics
Disease Surrogate Clinical Hypertension Blood Stroke Outcome Outcome Pressure
Time Time Model for failure: the surrogate is Model for failure: the surrogate is not in the causal pathway of the not in the causal pathway of the disease process disease process
Fluoride Intervention
Surrogate Bone Density Outcome Clinical Disease Outcome Osteoporosis fractures
Model for failure: the intervention Model for failure: the intervention affects only the pathway mediated affects only the pathway mediated through the surrogate through the surrogate
Intervention Protein Restriction
Clinical Kidney Surrogate Proteinuria Outcome Outcome Failure Disease Kidney Damage Model for failure: The intervention Example: Dihydropyridine calcium has several mechanisms of action channel blockers
Intervention Calcium Channel Blockers
Surrogate Clinical + Disease Outcome Outcome Blood Myocardial ASCVD _ Pressure Infarction
The Cardiac Arrhythmia Suppression CAST Research Question Trial (CAST*): Background • Ventricular arrhythmias are a risk factor Does suppression of for sudden death after MI • Four fold higher risk of cardiac mortality ventricular ectopy after a among persons with frequent premature MI reduce the incidence of ventricular contractions (PVCs) • In the CAST pilot study, the sudden death? antiarrhythmic drugs (encainide, flecainide) suppressed PVCs
*Echt DS et al. Mortality and morbidity in patients receiving encainide, flecainide, or placebo. NEJM 1991: 324(12): 781-8. CAST Design • Design: randomized trials of – encainide vs placebo – flecainide vs placebo • Participants (n=1498) – recent MI (6 days to 2 years ago) – ventricular ectopy (6 or more PVCs /hr) – at least 80% suppression of PVCs by active drug during open label titration period prior to randomization Source: Echt DS, Liebson PR, Mitchell B, et al. Mortality and morbidity in patients receiving encainide, flecainide, or placebo. The Cardiac Arrhythmia Suppression Trial. NEJM 1991: 324(12): 781-8.
CAST results: number of deaths Lessons from CAST and cardiac arrests by group • Active treatment: 63 events / 755 • Active treatments can be harmful (one of • Placebo: 26 events / 743 several recent trials in which placebo was superior to active treatment) p = 0.0001 • Reliance on surrogate outcomes can be misleading • same pattern of results for • The scientific community should encourage – death from arrhythmia researchers and sponsors to conduct studies – death from any cardiac cause with ‘hard’ clinical outcomes – death from any cause Examples from the Field Model for potential success: Surrogate outcome in the casual pathway • Surrogate that go in that go the right direction (easy to explain –fit your hypothesis) Diet Change • Surrogates that go in unexpected directions (create a greater need for hand-waving and but can still be made to fit your hypothesis) ↑ oxidative ↓ oxidative ASCVD stress stress • Surrogates that behave badly Time
Dietary Patterns
Dietary Antioxidants LDL Cholesterol Vitamin C Vitamin C Vitamin E beta-carotene
Oxidized LDL β-carotene
Alpha-tocopherol Free Radical Activity Oxidative stress Inflammatory Markers Markers Fatty Streak Formation
Atherosclerosis Figure 2b Nurses Health Study
• Design: Prospective Cohort Study • Participants: 121,700 female nurses free of diagnosed cardiovascular disease • Exposure Dietary questionnaire at baseline Assessment Vitamin E and Multivitamin Use • Follow-up: 8 years • End Points: 1) Major Coronary Disease 2) Non-fatal MI 3) Deaths Due to Coronary Disease
N Engl J Med 1993;328:1444-1449
Prospective observational studies of vitamin E: Effects on cardiovascular end points
N Engl J Med 1993;328:1444-1449 Adapted from: Jha, P. et. al. Ann Intern Med 1995;123:860-872 Summary of Biological Evidence
• Antioxidants are necessary
• Oxidized lipids are associated with CVD
• Oxidation of lipids is reduced by antioxidant supplementation
• Does supplementation lower risk of CVD? – Observational studies –trials
Do Vitamin E supplements Clinical Trials – Clinical reduce risk? Outcomes • Observational studies are confounded –vitamin E takers exercise more, have a lower BMI, eat healthier • Cardiovascular Events diets and smoke less often that non-vitamin users – Fatal and Non-fatal MI • Observational studies are hypothesis generating –Stroke – Peripheral artery disease • Surrogate markers are only indirectly related to clinical events • Mortality • Benefits can only be assessed in randomized controlled clinical trials ATBC Study • Design: Randomized, double-blind, placebo- controlled primary prevention trial • Participants: 29,133 male Finnish smokers, age 50-69 • Intervention: 1) Vitamin E 50 IU/day 2) B-carotene 20 mg/day 3) Combination 4) Placebo • Follow-up: 5-8 years • End Points: Incident lung cancer & deaths ATBC, 1993 NEJM
ATBC Trial Results CARET Study
• Design: Randomized, double blind, placebo- • Beta-carotene group (20 mg/day) controlled primary prevention – increase in total mortality (9%) trial – increased incidence of angina (13%)* • Participants: 18,314 smokers, former smokers, and workers exposed to asbestos – increased CVD mortality (11%)* • Intervention: 1) B-carotene (30 mg/day) and – increased incidence of lung cancer (18%) vitamin A (25,000 IU/day) 2) Placebo • Vitamin E Group (50 mg/day) • Follow-up: 4 years – reduction in total coronary events (3%) • End Points: Incident lung cancer – reduction in incident angina (9%) Cardiovascular Disease – reduction in non-fatal MI (11%) Omenn, 1996 NEJM
ATBC, 1994 NEJM Failed surrogate marker: example
β- carotene supplements + ↑β-carotene ↑Lung Smoking _ Cancer ↓β-carotene
Need for reliable surrogate markers Disadvantages of Surrogate The Bottom Line Outcomes (continued) • The relationship between a surrogate “Trust but verify” outcome and a clinical outcome has face validity but is often uncertain Ronald Reagan • Relationship between change in surrogate and risk of clinical outcomes is rarely known
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Analytic Techniques: Analytical Issues Crude analyses • Sample Size (Power Calculations) • Analysis depends on the type of outcome data •Basic tests • Analytical Approach (a priori) – Continuous outcome variable:t-test • Examples: Blood pressure, serum cholesterol • Intention-to-treat (vs ‘as treated’) – Dichotomous or categorical data: chi-squared, logistic regression, cox modeling for time to event • Example: Incident HIV, MI, cancer, renal failure, death
95 96 Analytic Techniques: Epidemiology in a box: The 2x2 table Adjusted (Regression) Analyses
D+ D- • Regression determines association between • The EXPOSURE (E) exposure and outcome – Example: obesity E+ aba+b • Procedures depends on outcome variable: • The OUTCOME (D) – Continuous outcome: linear regression – Example: Hypertension E- cdc+d – Dichotomous outcome: logistic regression • Applicable to most – Time-to-event: Cox proportional hazards study designs a+c b+d Total
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Interpretation of Results Why RCTs Can Be Difficult • Hard to find and recruit the right people • Internal Validity – Many don’t want to be “guinea pigs” – conclusions correctly describe what • Greater responsibility, documentation happened in the study • May take years for outcomes to develop • External Validity (‘generalizability’) • People are free to do as they please – Some assigned to treatment don’t adhere – the degree to which the conclusions apply to the study population and other – Some assigned to control seek treatment populations – Some drop out of the trial completely
99 Adherence (compliance) Why be worried about adherence?
• Difficult to measure Active
Drop-In’s • Difficult to promote Drop-Out’s • Must be promoted and measured, at least in efficacy or explanatory trials Control
Intention-to-Treat: analysis by randomized group,
101 not by final groupings 102
Adherence (compliance) How To Handle Participants Who • Measurement Don’t Adhere to Trial Assignment
– self report • Intention-to-Treat Approach – pill count – Least optimistic – blood levels of drug – Maintains initial balance from randomization – biological changes (urine or blood) – Highlights problems from adverse effects •Promotion • On-Treatment Approach – Most optimistic – exclude poor candidates before randomization – Upsets initial balance from randomization – keep intervention simple – Downplays problems from adverse effects – respond to evidence of inadequate adherence
103 Because of its conservatism, the Intention-to-Treat approach is strongly preferred. Cardiac Event-Free Survival in 192 Adults with Cardiac Event-Free Survival in 192 Adults with Refractory Angina by Random Assignment and Refractory Angina by Random Assignment and Cross-Over (from Medical Treatment to TMR) Status Cross-Over (from Medical Treatment to TMR) Status
Randomized to TMR, no crossing over to Medical Rx Were X-overs reclassified Randomized to as “TMR”, it Medical Rx, did would tend to poorly, needed TMR make TMR as last ditch effort look worse Randomized to Medical Rx, did OK, no need for TMR
TMR =transmyocardial laser revascularization
Cardiac Event-Free Survival in 192 Adults with Refractory Angina by Random Assignment and Cross-Over (from Medical Treatment to TMR) Status
Clinical Trials: Design and interpretation Considerations
Were X-overs classified as “Medical Rx”, it would tend to make Medical Rx look better 108 When Trials Are Impossible Statistical vs Clinical (or Nearly Impossible) Significance • Statistical significance pertains to • Adverse Exposures (e.g. Cigarettes) whether or not the observed results • Rare Outcomes (e.g. Reye’s Syndrome) could occur from chance alone • Intervention Already in Wide Use
In these circumstances, one must rely on observational studies—i.e. • Clinical significance pertains to whether prospective cohort studies and case-control studies. When interventions are or not the observed results have already in wide use, “outcomes research” is a good option. In outcomes research, medical interventions (e.g. drugs, surgical procedures) are “important” clinical, research or public considered as exposures. Data on these interventions, and on relevant clinical outcomes, are available from medical records and often from large-scale health relevance. electronic databases. 110
How To Interpret Negative Results Efficacy (Explanatory) Trial vs Effectiveness (Pragmatic) Trial • Treatment is worthless • Treatment is worthwhile, BUT study had… • Theory – Bias against the treatment (e.g. crossing in) – Efficacy: What is the effect of the therapy under ideal conditions – Inadequate contrast between groups – Effectiveness: What is the effect of therapy • Suboptimal treatment (e.g. unskilled surgeons) under ‘real world’ conditions • Low adherence (e.g. drug causes GI distress) • Controls sought treatment despite assignment • Reality – Insufficient statistical power – The dichotomy between efficacy and effectiveness is artificial • Very common cause of negative findings – Broad continuum • Meta-analysis a potential remedy 112 Typical Implementation Units • Clinical Centers Oversight Units – recruit participants – collect data • Internal – administer intervention/therapy – Sponsor • Laboratory or Reading Centers – Data Coordinating Center or Contract – perform assays or readings of procedures Research Organization • Data Coordinating Center* • External – receive/assemble data – Institutional Review Board – coordinate activities – Data and Safety Monitoring Board – perform data analyses
* similar to Contract Research Organization (CRO) 113 114
Organizational Structure of a Multi-Center Trial (Weight Loss Maintenance Trial)
Steering Committee NIH Project Office DSMB
Subcommittees Clinical Centers Coordinating Center
Center for Health Johns Hopkins Design & Analysis Enrollment and Research University Retention Data Intervention Management Development Publications
Intervention Pennington LSU Duke University
Measurement & Quality Control Minority Implementation
Clinic Coordinators
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