A roadmap to using randomization in clinical trials

Research Square

Abstract

Abstract 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.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.

Authors 13

1. Vance W. Berger

   National Institutes of Health

   Affiliation as printed

   National Institutes of Health

2. Louis Bour

   Boehringer Ingelheim (Germany)

   Affiliation as printed

   Boehringer Ingelheim (Germany)

3. Kerstine Carter

   Boehringer Ingelheim (United States)

   Affiliation as printed

   Boehringer Ingelheim (United States)

4. Jonathan Chipman

   University of Utah

   Affiliation as printed

   University of Utah

5. Colin Charles Everett

   University of Leeds

   Affiliation as printed

   University of Leeds

6. Nicole Maria Heussen

   Sigmund Freud Privatuniversität Wien

   Affiliation as printed

   Sigmund Freud University Vienna

7. Catherine Elizabeth Hewitt

   University of York

   Affiliation as printed

   University of York

8. R.-D Hilgers Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

9. Yuqun Luo

   United States Food and Drug Administration

   Affiliation as printed

   United States Food and Drug Administration

10. Jone Renteria

    Universitat de Barcelona

    Affiliation as printed

    University of Barcelona

11. Yevgen V. Ryeznik

    AstraZeneca (Sweden)

    Affiliation as printed

    AstraZeneca (Sweden)

12. Oleksandr Sverdlov corresponding

    Novartis (United States)

    Affiliation as printed

    Novartis (United States)

13. Diane Uschner

    George Washington University

    Affiliation as printed

    George Washington University