Abstract:
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The FDA's Guidance for Industry: E9 Statistical Principles for Clinical Trials presents unrestricted, restricted, and dynamic randomization methods as acceptable. However, unrestricted randomization and stratification using only a few key variables has been shown to be highly inefficient and have a much higher risk of serious imbalance in important covariates than matched randomization, which incorporates all important covariates. Matched randomization can handle cluster randomized trials, multi-arm trials, and be used in trials with enrollment coming all at once or sequentially. Matched randomization has been shown to dramatically improve covariate balance, yielding greater statistical power and more persuasive studies. This talk presents new research on matched randomization with sequential entry including introducing a dynamic matching threshold and Treatment Dependent Matching. Under various simulations settings and with real trial data, we compare the efficiency to estimate a treatment effect using a covariate-adjusted linear model following these extensions versus unrestricted or stratified randomization.
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