Adaptive allocation in randomized controlled trials

Abstract

Adaptive allocation has been proposed as a procedure to reduce the risk of chance imbalance of important prognostic factors in randomized controlled trials when the number of prognostic factors is large. In this article, minimization, a type of adaptive allocation, is compared to simple randomization and stratified allocation in a series of Monte Carlo simulations. Three outcomes are studied: estimated treatment effect, size of the rejection region, and power. Minimization produced an unbiased estimate of treatment effect and increased power when compared to simple randomization. Student's t test was conservative for both minimization and stratified allocation. Minimization and stratification produced similar improvements in power but there was some evidence that minimization might produce higher power than stratification when some prognostic variables cannot be included in the stratified allocation scheme.