Optimal designs for evaluating a series of treatments.
Several articles in this journal have studied optimal designs for testing a series of treatments to identify promising ones for further study. These designs formulate testing as an ongoing process until a promising treatment is identified. This formulation is considered to be more realistic but substantially increases the computational complexity. In this article, we show that these new designs, which control the error rates for a series of treatments, can be reformulated as conventional designs that control the error rates for each individual treatment. This reformulation leads to a more meaningful interpretation of the error rates and hence easier specification of the error rates in practice. The reformulation also allows us to use conventional designs from published tables or standard computer programs to design trials for a series of treatments. We illustrate these using a study in soft tissue sarcoma.
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA. leung@biost.mskcc.org
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D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D017322:Clinical Trials, Phase II as Topic / Q000706:statistics & numerical data; D006801:Humans; D012509:Sarcoma / Q000188:drug therapy; D012983:Soft Tissue Neoplasms / Q000188:drug therapy; D013812:Therapeutics / Q000706:statistics & numerical data*