Abstract:
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In clinical trials, patients with different biomarker features may respond differently to the new treatments or drugs. In personalized medicine, it is important to study the interaction between treatment and biomarkers in order to clearly identify patients that benefit from the treatment. With the local partial likelihood estimation (LPLE) method proposed by Fan et al. (2006), the treatment effect can be modelled as a flexible function of the biomarker. In this paper, we develop a local partial likelihood bootstrap test (LPLB) procedure for survival outcome data based on the LPLE, for assessing whether the treatment effect is a constant among all patients or varies as a function of the biomarker. In numerical simulations, we evaluate the finite sample properties of the LPLB test and compare it with the Cox regression model with a simple interaction and the existing Subpopulation Treatment Effect Pattern Plot (STEPP) method. We use data from a breast cancer and a prostate cancer clinical trial to illustrate the proposed LPLB test.
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