For many medical conditions there are several treatment options available to patients. We consider evaluating markers based on a simple treatment selection policy that incorporates information on the patient's marker value exceeding a threshold. Although traditional regression methods may assess the effect of the marker and treatment on outcomes, it is appealing to quantify more directly the potential impact on the population of using the marker to select treatment. A useful tool is the selection impact (SI) curve proposed by Song and Pepe (2004, \textit{Biometrics} \textbf{60}, 874--883) for binary outcomes. However, this approach does not deal with continuous outcomes, nor does it adjust for other covariates that are important for treatment selection. In this paper, we propose the SI curve for general outcomes, with specific focus on the survival time. We further propose the covariate specific SI curve to incorporate covariate information in treatment selection. Nonparametric and semiparametric estimators are developed accordingly. We show that the proposed estimators are consistent and asymptotically normal. Simulation studies demonstrate that these estimators work well with realistic sample sizes. We illustrate the SI curve and the statistical inference for it with data from an AIDS clinical trial.



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