Using Real‐World Data to Extrapolate Evidence From Randomized Controlled Trials

Randomized controlled trials (RCTs) provide evidence for regulatory agencies, shape clinical practice, influence formulary decisions, and have important implications for patients. However, many patient groups that are major consumers of drugs are under‐represented in randomized trials. We review three methods to extrapolate evidence from trial participants to different target populations following market approval and discuss how these could be implemented in practice to support regulatory and health technology assessment decisions. Although these methods are not a substitute for less restrictive pre‐approval RCTs or rigorous observational studies when sufficient data are available in the post‐approval setting, they can help to fill the evidence gap that exists in the early marketing period. Early evidence using real‐world data and methods for extrapolating evidence should be reported with clear explanation of assumptions and limitations especially when used to support regulatory and health technology assessment decisions.

[1]  J. Gagne,et al.  Simulation for Predicting Effectiveness and Safety of New Cardiovascular Drugs in Routine Care Populations , 2018, Clinical pharmacology and therapeutics.

[2]  Florence T. Bourgeois,et al.  Exclusion of Elderly People from Randomized Clinical Trials of Drugs for Ischemic Heart Disease , 2017, Journal of the American Geriatrics Society.

[3]  Nicholas C. Henderson,et al.  Cross-design synthesis for extending the applicability of trial evidence when treatment effect is heterogenous: Part II. Application and external validation , 2017 .

[4]  Issa J Dahabreh,et al.  Using group data to treat individuals: understanding heterogeneous treatment effects in the age of precision medicine and patient-centred evidence. , 2016, International journal of epidemiology.

[5]  Issa J Dahabreh,et al.  Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials. , 2016, International journal of epidemiology.

[6]  G. Pacini,et al.  Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus , 2016, Endocrine reviews.

[7]  A. Kunselman,et al.  Exclusion of Women of Childbearing Potential in Clinical Trials of Type 2 Diabetes Medications: A Review of Protocol-Based Barriers to Enrollment , 2016, Diabetes Care.

[8]  Catherine P. Bradshaw,et al.  Assessing the Generalizability of Randomized Trial Results to Target Populations , 2015, Prevention Science.

[9]  Christian Ohmann,et al.  Combining randomized and non‐randomized evidence in clinical research: a review of methods and applications , 2015, Research synthesis methods.

[10]  R J Glynn,et al.  Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs , 2014, Journal of internal medicine.

[11]  David M. Kent,et al.  Using Internally Developed Risk Models to Assess Heterogeneity in Treatment Effects in Clinical Trials , 2014, Circulation. Cardiovascular quality and outcomes.

[12]  Bruce H Fireman,et al.  Confounding Adjustment in Comparative Effectiveness Research Conducted Within Distributed Research Networks , 2013, Medical care.

[13]  Sebastian Schneeweiss,et al.  Active safety monitoring of newly marketed medications in a distributed data network: application of a semi-automated monitoring system , 2012, Clinical pharmacology and therapy.

[14]  H. Eichler,et al.  Adaptive Licensing: Taking the Next Step in the Evolution of Drug Approval , 2012, Clinical pharmacology and therapeutics.

[15]  Eloise E Kaizar.  Estimating treatment effect via simple cross design synthesis , 2011, Statistics in medicine.

[16]  Malcolm Rowland,et al.  Bridging the efficacy–effectiveness gap: a regulator's perspective on addressing variability of drug response , 2011, Nature Reviews Drug Discovery.

[17]  Catherine P. Bradshaw,et al.  The use of propensity scores to assess the generalizability of results from randomized trials , 2011, Journal of the Royal Statistical Society. Series A,.

[18]  David M Kent,et al.  Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal , 2010, Trials.

[19]  S. Cole,et al.  Generalizing evidence from randomized clinical trials to target populations: The ACTG 320 trial. , 2010, American journal of epidemiology.

[20]  D. Goldberg,et al.  Disparate inclusion of older adults in clinical trials: priorities and opportunities for policy and practice change. , 2010, American journal of public health.

[21]  R. Hayward,et al.  Beyond the Randomized Clinical Trial the Role of Effectiveness Studies in Evaluating Cardiovascular Therapies the Achilles' Heel of Rcts Key Issues in Outcomes Research , 2022 .

[22]  D. Charytan,et al.  The exclusion of patients with chronic kidney disease from clinical trials in coronary artery disease. , 2006, Kidney international.

[23]  J. Avorn,et al.  A review of uses of health care utilization databases for epidemiologic research on therapeutics. , 2005, Journal of clinical epidemiology.

[24]  Alex J. Sutton,et al.  Bayesian methods for the cross‐design synthesis of epidemiological and toxicological evidence , 2005 .

[25]  C. Guestrin,et al.  Distributed regression: an efficient framework for modeling sensor network data , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[26]  David R. Jones,et al.  Hierarchical models in generalized synthesis of evidence: an example based on studies of breast cancer screening. , 2000, Statistics in medicine.

[27]  J. Avorn.  Including elderly people in clinical trials , 1997, BMJ.

[28]  William White,et al.  A Proposal , 2008, Moon, Sun, and Witches.