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Erschienen in:
2011 | OriginalPaper | Buchkapitel
RCTs with Time-to-Event Outcomes and Effect Modification Parameters
Erschienen in: Targeted Learning
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Current methods used to evaluate effect modification in time-to-event data, such as the Cox proportional hazards model or its discrete time analog the logistic failure time model, posit highly restrictive parametric statistical models and attempt to estimate parameters that are specific to the model proposed. These methods, as a result of their parametric nature, tend to be biased and force practitioners to estimate parameters that are convenient rather than parameters they are actually interested in estimating. The TMLE improves on the currently implemented methods in both robustness, its ability to provide unbiased estimates, and flexibility, allowing practitioners to estimate parameters that directly answer their question of interest.