A roadmap to using randomization in clinical trials.

Autor: Berger VW; National Institutes of Health, Bethesda, MD, USA., Bour LJ; Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany., Carter K; Boehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, USA., Chipman JJ; Population Health Sciences, University of Utah School of Medicine, Salt Lake City UT, USA.; Cancer Biostatistics, University of Utah Huntsman Cancer Institute, Salt Lake City UT, USA., Everett CC; Clinical Trials Research Unit, University of Leeds, Leeds, UK., Heussen N; RWTH Aachen University, Aachen, Germany.; Medical School, Sigmund Freud University, Vienna, Austria., Hewitt C; York Trials Unit, Department of Health Sciences, University of York, York, UK., Hilgers RD; RWTH Aachen University, Aachen, Germany., Luo YA; Food and Drug Administration, Silver Spring, MD, USA., Renteria J; Open University of Catalonia (UOC) and the University of Barcelona (UB), Barcelona, Spain.; Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA., Ryeznik Y; BioPharma Early Biometrics & Statistical Innovations, Data Science & AI, R&D BioPharmaceuticals, AstraZeneca, Gothenburg, Sweden., Sverdlov O; Early Development Analytics, Novartis Pharmaceuticals Corporation, NJ, East Hanover, USA. alex.sverdlov@novartis.com., Uschner D; Biostatistics Center & Department of Biostatistics and Bioinformatics, George Washington University, DC, Washington, USA.
Jazyk: angličtina
Zdroj: BMC medical research methodology [BMC Med Res Methodol] 2021 Aug 16; Vol. 21 (1), pp. 168. Date of Electronic Publication: 2021 Aug 16.
DOI: 10.1186/s12874-021-01303-z
Abstrakt: Background: Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial.
Methods: We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice.
Results: Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size.
Conclusions: The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.
(© 2021. The Author(s).)
Databáze: MEDLINE
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