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Abstract:
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In this talk, we introduce a statistical method for optimal treatment selection for a subgroup of patients, using their biomarker values based on casual inference. This new method is based on the conditional quantile treatment effect (CQTE) curve, and its simultaneous confidence bands (SCBs), which could be used to represent the quantile treatment effect for a given value of the covariate (biomarker) and to select an optimal treatment for one particular patient. We then propose B-splines methods for estimating the CSTE curves and constructing simultaneous confidence bands for the CQTE curves. We derive the asymptotic properties of the proposed methods. We also conduct extensive simulation studies to evaluate finite-sample properties of the proposed simultaneous confidence bands. Finally, we illustrate the application of the CQTE curve and its simultaneous confidence bands in optimal treatment selection in a real-world data set. This is a joint work with Kaishan Han.
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