Journal
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume 94, Issue 1, Pages 97-119Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/S0378-3758(00)00228-7
Keywords
clinical trials; imbalance minimization; minimum likelihood allocation; simulation; logistic regression; complete randomization
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Few topics have stirred as much discussion and controversy as randomization. A reading of the literature suggests that clinical trialists generally feel randomization is necessary for valid inference, while biostatisticians using model-based inference often appear to prefer nearly optimal designs irrespective of any induced randomness. Dissection of the methods of treatment assignment shows that there are five basic approaches; pure randomizers, true randomizers, quasi-randomizers, permutation testers, and conventional modelers. Four of these have coherent design and analysis strategies, even though they are not mutually consistent, but the fifth and most prevalent approach (quasi-randomization) has little to recommend it. Design-adaptive allocation is defined, it is shown to provide valid inference, and a simulation indicates its efficiency advantage. In small studies, or large studies with many important prognostic covariates or analytic subgroups, design-adaptive allocation is an extremely attractive method of treatment assignment. (C) 2001 Elsevier Science B.V. All rights reserved.
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