Adaptive multicenter designs for continuous response clinical trials in the presence of an unknown sensitive subgroup
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The partial effectiveness of drugs is of importance to the pharmaceutical industry. Randomized controlled trials (RCTs) assuming the existence of a subgroup sensitive to the treatment are already used. These designs, however, are available only if there is a known marker for identifying subjects in the subgroup. In this paper we investigate a model in which the response in the treatment group Z^T has a two-component mixture density (1-p) N(μ^C, σ^2)+p N(μ^T, σ^2) representing the treatment responses of placebo responders and drug responders. The treatment-specific effect is μ = μ^T-μ^C/σ and p is the prevalence of the drug responders in the population. Other patients in the treatment group react as if they had received a placebo. We develop one- and two-stage RCT designs that are able to detect a sensitive subgroup based solely on the responses. We also extend them to a multicenter RCTs using Hochberg's step-up procedure. We avoid extensive simulations and use simple and quick numerical optimization methods.
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