Analysis of randomized comparative clinical trial data for personalized treatment selections

Suppose that under the conventional randomized clinical trial setting, a new therapy is compared with a standard treatment.In this article, we propose a systematic, 2-stage estimation procedure for the subject-level treatment differences for future patient's disease management and treatment selections.To construct this procedure, we first utilize a parametric or semiparametric method to estimate individual-level treatment differences, and use these estimates to create an index scoring system for grouping patients.We then consistently estimate the average treatment difference for each subgroup of subjects via a nonparametric function estimation method.Furthermore, pointwise and simultaneous interval estimates are constructed to make inferences about such subgroup-specific treatment differences.The new proposal is illustrated with the data from a clinical trial for evaluating the efficacy and toxicity of a 3-drug combination versus a standard 2-drug combination for treating HIV-1.infected patients.