Title: A Simulation-based Framework for Pragmatic Trial Design Using Observational Data.
Abstract: Design of clinical trials requires careful decision-making across several dimensions, including endpoints, eligibility criteria, and subgroup enrichment. Clinical trial simulation can be an informative tool in trial design, providing empirical evidence by which to evaluate and compare the results of hypothetical trials with varying designs. In this paper, we introduce a novel simulation-based approach using observational data to inform the design of a future pragmatic trial. To account for likely confounding by indication, we utilize propensity score-adjusted models to simulate hypothetical trials under alternative endpoints and enrollment criteria. We apply our approach to the design of pragmatic trials in psoriatic arthritis, using observational data embedded within the Tight Control of Inflammation in Early Psoriatic Arthritis study to simulate hypothetical open-label trials comparing treatment with tumor necrosis factor-α inhibitors to methotrexate. We first validate our simulations of a trial with traditional enrollment criteria and endpoints against a recently published trial. Next, we compare simulated treatment effects in patient populations defined by traditional and broadened enrollment criteria, where the latter is consistent with a future pragmatic trial. In each trial, we also consider five candidate primary endpoints. Our results highlight how changes in the enrolled population and primary endpoints may qualitatively alter study findings and the ability to detect heterogeneous treatment effects between clinical subgroups. These considerations, among others, are important for designing a future pragmatic trial aimed at having high external validity with relevance for real-world clinical practice. Our approach may be generalized to the study of other conditions where existing trial data are limited or do not generalize well to real-world clinical practice, but where observational data are available.
Alisa J. Stephens-Shields is an Assistant Professor of Biostatistics at the University of Pennsylvania Perelman School of Medicine. Her research focuses on flexible and efficient analysis of data from cluster-randomized trials and other extensions of causal inference methodology to enhance the design and analysis of clinical trials. She also works in the development of patient-reported outcomes to inform population-appropriate trial endpoints. Dr. Stephens-Shields collaborates in several areas, including pediatrics, chronic pain, pharmacoepidemiology, and behavioral economics. She is a recipient of the inaugural Committee of Presidents of Statistical Societies Leadership Academy award and has held elected positions in the American Statistical Association Section on Statistics in Epidemiology and the Eastern North American Region of the International Biometrics Society. Dr. Stephens-Shields serves as an associate editor of Biostatistics and a statistical consultant for the Annals of Internal Medicine. She holds Ph.D. and A.M. degrees in biostatistics from Harvard University and a B.S. in mathematics with minor in Spanish from the University of Maryland, College Park.