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Abstract:
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In many medical studies, estimation of treatment effects is often of primary scientific interest. Standard methods for evaluating the treatment effect in survival analysis typically require the assumption of independent censoring. Such an assumption might be invalid in many medical studies, where the presence of dependent censoring leads to difficulties in analyzing covariate effects on disease outcomes. This data structure is called 'semicompeting risks data'. In modeling semicompeting risks data, an artificial censoring technique has been useful in handling dependent censoring. However, continuous covariates with large variability may lead to excessive artificial censoring, which subsequently results in numerically unstable estimation. In this paper, we propose a novel strategy for weighted estimation of treatment effects in the accelerated failure time model, where weights are based on the propensity score. This novel application of propensity scores avoids excess artificial censoring caused by continuous covariates and simplifies computation. Monte Carlo simulation studies and application to a prostate cancer clinical trial are used to illustrate the methodology.
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