OMF Literature Review Report
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From Explanatory to Pragmatic Clinical Trials: a New Era for Effectiveness Research
From Explanatory to Pragmatic Clinical Trials: A New Era for Effectiveness Research Jerry Jarvik, M.D., M.P.H. Professor of Radiology, Neurological Surgery and Health Services Adjunct Professor Orthopedic Surgery & Sports Medicine and Pharmacy Director, Comparative Effectiveness, Cost and Outcomes Research Center (CECORC) UTSW April 2018 Acknowledgements •NIH: UH2 AT007766; UH3 AT007766 •NIH P30AR072572 •PCORI: CE-12-11-4469 Disclosures Physiosonix (ultrasound company): Founder/stockholder UpToDate: Section Editor Evidence-Based Neuroimaging Diagnosis and Treatment (Springer): Co-Editor The Big Picture Comparative Effectiveness Evidence Based Practice Health Policy Learning Healthcare System So we need to generate evidence Challenge #1: Clinical research is slow • Tradi>onal RCTs are slow and expensive— and rarely produce findings that are easily put into prac>ce. • In fact, it takes an average of 17 ye ars before research findings Howlead pragmaticto widespread clinical trials canchanges improve in care. practice & policy Challenge #1: Clinical research is slow “…rarely produce findings that are easily put into prac>ce.” Efficacy vs. Effectiveness Efficacy vs. Effectiveness • Efficacy: can it work under ideal conditions • Effectiveness: does it work under real-world conditions Challenge #2: Clinical research is not relevant to practice • Tradi>onal RCTs study efficacy “If we want of txs for carefully selected more evidence- popula>ons under ideal based pr actice, condi>ons. we need more • Difficult to translate to real practice-based evidence.” world. Green, LW. American Journal • When implemented into of Public Health, 2006. How pragmatic clinical trials everyday clinical prac>ce, oZen seecan a “ voltage improve drop practice”— drama>c & decreasepolicy from efficacy to effec>veness. -
Impact of Blinding on Estimated Treatment Effects in Randomised Clinical Trials
RESEARCH Impact of blinding on estimated treatment effects in randomised BMJ: first published as 10.1136/bmj.l6802 on 21 January 2020. Downloaded from clinical trials: meta-epidemiological study Helene Moustgaard,1-4 Gemma L Clayton,5 Hayley E Jones,5 Isabelle Boutron,6 Lars Jørgensen,4 David R T Laursen,1-4 Mette F Olsen,4 Asger Paludan-Müller,4 Philippe Ravaud,6 5,7 5,7,8 5,7 1-3 Jelena Savović, , Jonathan A C Sterne, Julian P T Higgins, Asbjørn Hróbjartsson For numbered affiliations see ABSTRACT 1 indicated exaggerated effect estimates in trials end of the article. OBJECTIVES without blinding. Correspondence to: To study the impact of blinding on estimated RESULTS H Moustgaard treatment effects, and their variation between [email protected] The study included 142 meta-analyses (1153 trials). (or @HeleneMoustgaa1 on Twitter trials; differentiating between blinding of patients, The ROR for lack of blinding of patients was 0.91 ORCID 0000-0002-7057-5251) healthcare providers, and observers; detection bias (95% credible interval 0.61 to 1.34) in 18 meta- Additional material is published and performance bias; and types of outcome (the analyses with patient reported outcomes, and 0.98 online only. To view please visit MetaBLIND study). the journal online. (0.69 to 1.39) in 14 meta-analyses with outcomes C ite this as: BMJ 2020;368:l6802 DESIGN reported by blinded observers. The ROR for lack of http://dx.doi.org/10.1136/bmj.l6802 Meta-epidemiological study. blinding of healthcare providers was 1.01 (0.84 to Accepted: 19 November 2019 DATA SOURCE 1.19) in 29 meta-analyses with healthcare provider Cochrane Database of Systematic Reviews (2013-14). -
Development of Validated Instruments
Development of Validated Instruments Michelle Tarver, MD, PhD Medical Officer Division of Ophthalmic and Ear, Nose, and Throat Devices Center for Devices and Radiological Health Food and Drug Administration No Financial Conflicts to Disclose 2 Overview • Define Patient Reported Outcomes (PROs) • Factors to Consider when Developing PROs • FDA Guidance for PROs • Use of PROs in FDA Clinical Trials 3 Patient Reported Outcomes (PROs) • Any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else • Can be measured in absolute terms (e.g., severity of a symptom) or as a change from a previous measure • In trials, measures the effect of a medical intervention on one or more concepts – Concept is the thing being measured (e.g., symptom, effects on function, severity of health condition) 4 Concepts a PRO May Capture • Symptoms • Symptom impact and functioning • Disability/handicap • Adverse events • Treatment tolerability • Treatment satisfaction • Health-related quality of life 5 Criteria to Consider in PRO Development • Appropriateness – Does the content address the relevant questions for the device? • Acceptability – Is the questionnaire acceptable to patients? • Feasibility – Is it easy to administer and process/analyze? • Interpretability – Are the scores interpretable? Abstracted from (1) Patient Reported Outcomes Measurement Group: University of Oxford (2) NIH PROMIS Instrument Development and Validation Standards 6 Criteria -
Using Randomized Evaluations to Improve the Efficiency of US Healthcare Delivery
Using Randomized Evaluations to Improve the Efficiency of US Healthcare Delivery Amy Finkelstein Sarah Taubman MIT, J-PAL North America, and NBER MIT, J-PAL North America February 2015 Abstract: Randomized evaluations of interventions that may improve the efficiency of US healthcare delivery are unfortunately rare. Across top journals in medicine, health services research, and economics, less than 20 percent of studies of interventions in US healthcare delivery are randomized. By contrast, about 80 percent of studies of medical interventions are randomized. The tide may be turning toward making randomized evaluations closer to the norm in healthcare delivery, as the relevant actors have an increasing need for rigorous evidence of what interventions work and why. At the same time, the increasing availability of administrative data that enables high-quality, low-cost RCTs and of new sources of funding that support RCTs in healthcare delivery make them easier to conduct. We suggest a number of design choices that can enhance the feasibility and impact of RCTs on US healthcare delivery including: greater reliance on existing administrative data; measuring a wide range of outcomes on healthcare costs, health, and broader economic measures; and designing experiments in a way that sheds light on underlying mechanisms. Finally, we discuss some of the more common concerns raised about the feasibility and desirability of healthcare RCTs and when and how these can be overcome. _____________________________ We are grateful to Innessa Colaiacovo, Lizi Chen, and Belinda Tang for outstanding research assistance, to Katherine Baicker, Mary Ann Bates, Kelly Bidwell, Joe Doyle, Mireille Jacobson, Larry Katz, Adam Sacarny, Jesse Shapiro, and Annetta Zhou for helpful comments, and to the Laura and John Arnold Foundation for financial support. -
Development of a Person-Centered Conceptual Model of Perceived Fatigability
Quality of Life Research (2019) 28:1337–1347 https://doi.org/10.1007/s11136-018-2093-z Development of a person-centered conceptual model of perceived fatigability Anna L. Kratz1 · Susan L. Murphy2 · Tiffany J. Braley3 · Neil Basu4 · Shubhangi Kulkarni1 · Jenna Russell1 · Noelle E. Carlozzi1 Accepted: 17 December 2018 / Published online: 2 January 2019 © Springer Nature Switzerland AG 2019 Abstract Purpose Perceived fatigability, reflective of changes in fatigue intensity in the context of activity, has emerged as a poten- tially important clinical outcome and quality of life indicator. Unfortunately, the nature of perceived fatigability is not well characterized. The aim of this study is to define the characteristics of fatigability through the development of a conceptual model informed by input from key stakeholders who experience fatigability, including the general population, individuals with multiple sclerosis (MS), and individuals with fibromyalgia (FM). Methods Thirteen focus groups were conducted with 101 participants; five groups with n = 44 individuals representing the general population, four groups with n = 26 individuals with MS, and four groups with n = 31 individuals with FM. Focus group data were qualitatively analyzed to identify major themes in the participants’ characterizations of perceived fatigability. Results Seven major themes were identified: general fatigability, physical fatigability, mental fatigability, emotional fati- gability, moderators of fatigability, proactive and reactive behaviors, and temporal aspects of fatigability. Relative to those in the general sample, FM or MS groups more often described experiencing fatigue as a result of cognitive activity, use of proactive behaviors to manage fatigability, and sensory stimulation as exacerbating fatigability. Conclusions Fatigability is the complex and dynamic process of the development of physical, mental, and/or emotional fatigue. -
(EPOC) Data Collection Checklist
Cochrane Effective Practice and Organisation of Care Review Group DATA COLLECTION CHECKLIST Page 2 Cochrane Effective Practice and Organisation of Care Review Group (EPOC) Data Collection Checklist CONTENTS Item Page Introduction 5-6 1 Inclusion criteria* 7-8 1.1 Study design* 7 1.1.1 Randomised controlled trial* 1.1.2 Controlled clinical trial* 1.1.3 Controlled before and after study* 1.1.4 Interrupted time series* 1.2 Methodological inclusion criteria* 8 2 Interventions* 9-12 2.1 Type of intervention 9 2.1.1 Professional interventions* 9 2.1.2 Financial interventions* 10 2.1.2.1 Provider interventions* 2.1.2.2 Patient interventions* 2.1.3 Organisational interventions* 11 2.1.3.1 Provider orientated interventions* 2.1.3.2 Patient orientated interventions* 2.1.3.3 Structural interventions* 2.1.4 Regulatory interventions* 12 2.2 Controls* 13 3 Type of targeted behaviour* 13 4 Participants* 14-15 4.1 Characteristics of participating providers* 14 4.1.1 Profession* Item Page Page 3 4.1.2 Level of training* 4.1.3 Clinical speciality* 4.1.4 Age 4.1.5 Time since graduation 4.2 Characteristics of participating patients* 15 4.2.1 Clinical problem* 4.2.2 Other patient characteristics 4.2.3 Number of patients included in the study* 5 Setting* 16 5.1 Reimbursement system 5.2 Location of care* 5.3 Academic Status* 5.4 Country* 5.5 Proportion of eligible providers from the sampling frame* 6 Methods* 17 6.1 Unit of allocation* 6.2 Unit of analysis* 6.3 Power calculation* 6.4 Quality criteria* 17-22 6.4.1 Quality criteria for randomised controlled trials -
Interpreting Indirect Treatment Comparisons and Network Meta
VALUE IN HEALTH 14 (2011) 417–428 available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/jval SCIENTIFIC REPORT Interpreting Indirect Treatment Comparisons and Network Meta-Analysis for Health-Care Decision Making: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 1 Jeroen P. Jansen, PhD1,*, Rachael Fleurence, PhD2, Beth Devine, PharmD, MBA, PhD3, Robbin Itzler, PhD4, Annabel Barrett, BSc5, Neil Hawkins, PhD6, Karen Lee, MA7, Cornelis Boersma, PhD, MSc8, Lieven Annemans, PhD9, Joseph C. Cappelleri, PhD, MPH10 1Mapi Values, Boston, MA, USA; 2Oxford Outcomes, Bethesda, MD, USA; 3Pharmaceutical Outcomes Research and Policy Program, School of Pharmacy, School of Medicine, University of Washington, Seattle, WA, USA; 4Merck Research Laboratories, North Wales, PA, USA; 5Eli Lilly and Company Ltd., Windlesham, Surrey, UK; 6Oxford Outcomes Ltd., Oxford, UK; 7Canadian Agency for Drugs and Technologies in Health (CADTH), Ottawa, ON, Canada; 8University of Groningen / HECTA, Groningen, The Netherlands; 9University of Ghent, Ghent, Belgium; 10Pfizer Inc., New London, CT, USA ABSTRACT Evidence-based health-care decision making requires comparisons of all of randomized, controlled trials allow multiple treatment comparisons of relevant competing interventions. In the absence of randomized, con- competing interventions. Next, an introduction to the synthesis of the avail- trolled trials involving a direct comparison of all treatments of interest, able evidence with a focus on terminology, assumptions, validity, and statis- indirect treatment comparisons and network meta-analysis provide use- tical methods is provided, followed by advice on critically reviewing and in- ful evidence for judiciously selecting the best choice(s) of treatment. terpreting an indirect treatment comparison or network meta-analysis to Mixed treatment comparisons, a special case of network meta-analysis, inform decision making. -
Patient Reported Outcomes (PROS) in Performance Measurement
Patient-Reported Outcomes Table of Contents Introduction ............................................................................................................................................ 2 Defining Patient-Reported Outcomes ............................................................................................ 2 How Are PROs Used? ............................................................................................................................ 3 Measuring research study endpoints ........................................................................................................ 3 Monitoring adverse events in clinical research ..................................................................................... 3 Monitoring symptoms, patient satisfaction, and health care performance ................................ 3 Example from the NIH Collaboratory ................................................................................................................... 4 Measuring PROs: Instruments, Item Banks, and Devices ....................................................... 5 PRO Instruments ............................................................................................................................................... 5 Item Banks........................................................................................................................................................... 8 Devices ................................................................................................................................................................. -
A National Strategy to Develop Pragmatic Clinical Trials Infrastructure
A National Strategy to Develop Pragmatic Clinical Trials Infrastructure Thomas W. Concannon, Ph.D.1,2, Jeanne-Marie Guise, M.D., M.P.H.3, Rowena J. Dolor, M.D., M.H.S.4, Paul Meissner, M.S.P.H.5, Sean Tunis, M.D., M.P.H.6, Jerry A. Krishnan, M.D., Ph.D.7,8, Wilson D. Pace, M.D.9, Joel Saltz, M.D., Ph.D.10, William R. Hersh, M.D.3, Lloyd Michener, M.D.4, and Timothy S. Carey, M.D., M.P.H.11 Abstract An important challenge in comparative effectiveness research is the lack of infrastructure to support pragmatic clinical trials, which com- pare interventions in usual practice settings and subjects. These trials present challenges that differ from those of classical efficacy trials, which are conducted under ideal circumstances, in patients selected for their suitability, and with highly controlled protocols. In 2012, we launched a 1-year learning network to identify high-priority pragmatic clinical trials and to deploy research infrastructure through the NIH Clinical and Translational Science Awards Consortium that could be used to launch and sustain them. The network and infrastructure were initiated as a learning ground and shared resource for investigators and communities interested in developing pragmatic clinical trials. We followed a three-stage process of developing the network, prioritizing proposed trials, and implementing learning exercises that culminated in a 1-day network meeting at the end of the year. The year-long project resulted in five recommendations related to developing the network, enhancing community engagement, addressing regulatory challenges, advancing information technology, and developing research methods. -
Meta-Analysis: Methods for Quantitative Data Synthesis
Department of Health Sciences M.Sc. in Evidence Based Practice, M.Sc. in Health Services Research Meta-analysis: methods for quantitative data synthesis What is a meta-analysis? Meta-analysis is a statistical technique, or set of statistical techniques, for summarising the results of several studies into a single estimate. Many systematic reviews include a meta-analysis, but not all. Meta-analysis takes data from several different studies and produces a single estimate of the effect, usually of a treatment or risk factor. We improve the precision of an estimate by making use of all available data. The Greek root ‘meta’ means ‘with’, ‘along’, ‘after’, or ‘later’, so here we have an analysis after the original analysis has been done. Boring pedants think that ‘metanalysis’ would have been a better word, and more euphonious, but we boring pedants can’t have everything. For us to do a meta-analysis, we must have more than one study which has estimated the effect of an intervention or of a risk factor. The participants, interventions or risk factors, and settings in which the studies were carried out need to be sufficiently similar for us to say that there is something in common for us to investigate. We would not do a meta-analysis of two studies, one of which was in adults and the other in children, for example. We must make a judgement that the studies do not differ in ways which are likely to affect the outcome substantially. We need outcome variables in the different studies which we can somehow get in to a common format, so that they can be combined. -
Evidence-Based Medicine: Is It a Bridge Too Far?
Fernandez et al. Health Research Policy and Systems (2015) 13:66 DOI 10.1186/s12961-015-0057-0 REVIEW Open Access Evidence-based medicine: is it a bridge too far? Ana Fernandez1*, Joachim Sturmberg2, Sue Lukersmith3, Rosamond Madden3, Ghazal Torkfar4, Ruth Colagiuri4 and Luis Salvador-Carulla5 Abstract Aims: This paper aims to describe the contextual factors that gave rise to evidence-based medicine (EBM), as well as its controversies and limitations in the current health context. Our analysis utilizes two frameworks: (1) a complex adaptive view of health that sees both health and healthcare as non-linear phenomena emerging from their different components; and (2) the unified approach to the philosophy of science that provides a new background for understanding the differences between the phases of discovery, corroboration, and implementation in science. Results: The need for standardization, the development of clinical epidemiology, concerns about the economic sustainability of health systems and increasing numbers of clinical trials, together with the increase in the computer’s ability to handle large amounts of data, have paved the way for the development of the EBM movement. It was quickly adopted on the basis of authoritative knowledge rather than evidence of its own capacity to improve the efficiency and equity of health systems. The main problem with the EBM approach is the restricted and simplistic approach to scientific knowledge, which prioritizes internal validity as the major quality of the studies to be included in clinical guidelines. As a corollary, the preferred method for generating evidence is the explanatory randomized controlled trial. This method can be useful in the phase of discovery but is inadequate in the field of implementation, which needs to incorporate additional information including expert knowledge, patients’ values and the context. -
Evidence-Based Medicine and Why Should I Care?
New Horizons Symposium Papers What Is Evidence-Based Medicine and Why Should I Care? Dean R Hess PhD RRT FAARC Introduction Is There a Problem? What Is Evidence-Based Medicine? Hierarchy of Evidence Finding the Evidence Examining the Evidence for a Diagnosis Test Examining the Evidence for a Therapy Meta-Analysis Why Isn’t the Best Evidence Implemented Into Practice? Summary The principles of evidence-based medicine provide the tools to incorporate the best evidence into everyday practice. Evidence-based medicine is the integration of individual clinical expertise with the best available research evidence from systematic research and the patient’s values and expec- tations. A hierarchy of evidence can be used to assess the strength of evidence upon which clinical decisions are made, with randomized studies at the top of the hierarchy. The efficient approach to finding the best evidence is to identify a systematic review or evidence-based clinical practice guidelines. Calculated metrics, such as sensitivity, specificity, receiver-operating-characteristic curves, and likelihood ratios, can be used to examine the evidence for a diagnostic test. High-level studies of a therapy are prospective, randomized, blinded, placebo-controlled, have a concealed allocation, have a parallel design, and assess patient-important outcomes. Metrics used to assess the evidence for a therapy include event rate, relative risk, relative risk reduction, absolute risk re- duction, number needed to treat, and odds ratio. Although not all tenets of evidence-based medicine are universally accepted, the principles of evidence-based medicine nonetheless provide a valuable approach to respiratory care practice. Key words: likelihood ratio, meta-analysis, number needed to treat, receiver operating characteristic curve, relative risk, sensitivity, specificity, systematic review, evidence-based medicine.