Abstract:
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The conditional survival function of a time-to-event outcome subject to right censoring is a common target of estimation in survival analysis. This parameter may be of scientific interest and also often appears as a nuisance in semiparametric settings. In addition to classical parametric and semiparametric methods (e.g., based on the Cox proportional hazards model), flexible machine learning approaches have been developed to estimate the conditional survival function. However, many of these methods are targeted toward risk stratification rather than function estimation. Others apply only to discrete time settings or require inverse probability of censoring weights, which can be as difficult to estimate as the outcome survival function itself. Here, we propose a novel decomposition of the conditional survival function in terms of observable regression models in which censoring plays no role. This allows application of an array of flexible regression and classification methods, possibly in combination, rather than only approaches that explicitly handle right censoring. We outline an estimation procedure based on this decomposition and assess its performance via numerical simulations.
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