Date of Award
2020
Document Type
Dissertation - MUSC Only
Degree Name
Doctor of Philosophy (PhD)
Department
Public Health Sciences
College
College of Graduate Studies
First Advisor
Viswanathan Ramakrishnan
Second Advisor
Wenle Zhao
Third Advisor
Paul J. Nietert
Fourth Advisor
Jody D. Ciolino
Fifth Advisor
Michael D. Hill
Abstract
When there is a large number of baseline covariates whose imbalance needs to be controlled in sequential randomized controlled trials, minimization is most commonly used for randomizing treatment assignments. The lack of allocation randomness associated with the minimization method has been the source of controversy. The minimal sufficient balance (MSB) method is an alternative to minimization. It prevents serious imbalance from a large number of covariates while maintaining high levels of allocation randomness. However, a formal comparison between covariate-adaptive methods of randomization has not yet been studied. Using a re-randomization of the rt-PA clinical trial dataset with 1:1 equal allocation, minimization and MSB methods are compared with respect to allocation randomness, effectiveness at balancing covariates across treatment arms, and preservation of the nominal type I error rate. Using a simulated dataset, power and bias in the estimation of treatment effect are studied for completely randomized design, stratified permuted blocks, minimization, and MSB. A novel randomization method, known as allocation ratio preserving Minimal Sufficient Balance (ARP MSB) is presented as an alternative to allocation ratio preserving biased coin minimization (ARP BCM). Using a re-randomization of the rt-PA clinical trial dataset, ARP BCM and ARP MSB are compared with respect to the allocation randomness, effectiveness at balancing covariates across treatment arms, and preservation of the nominal type I error rate in unequal allocation clinical trials. MSB and ARP MSB methods proved to have equal or superior effectiveness at controlling imbalance on a combination of continuous and categorical variables, as well as a far greater proportion of completely random treatment assignments compared to the minimization and ARP BCM methods. MSB, ARP MSB, minimization, and ARP BCM all proved to have similar properties with respect to type I error rate preservation, power, and bias in measuring treatment effects. MSB and ARP MSB, while not presented as optimal methods for controlling covariate imbalances in sequential clinical trials, provide an alternative to the minimization and ARP BCM methods. The arguments in this dissertation should be considered by those who wish to use minimization or ARP BCM for subject allocation in clinical trials.
Recommended Citation
Lauzon, Steven Daniel, "Evaluation of the Statistical Properties of Minimal Sufficient Balance as a Method
for Controlling Baseline Covariate Imbalance for Sequential Clinical Trials with a
Binary Endpoint" (2020). MUSC Theses and Dissertations. 597.
https://medica-musc.researchcommons.org/theses/597
Rights
All rights reserved. Copyright is held by the author.