Speaker
Description
Occam’s Razor suggests that, among several plausible explanations for a phenomenon, the simplest is preferable. Applied to regression analysis, this implies that the smallest model that fits the data is best. Therefore, in terms of analyzing high-dimensional time-to-event data, variable selection techniques are required, if we want to follow the principle of Occam's Razor. A widely used approach is Lasso regularization, but inference after Lasso selection remains challenging, particularly for complex models such as the Cox proportional hazards model, where standard confidence intervals and p-values are not readily available.
We compared proposals for selective inference targeting the submodel parameters of the Lasso and its extension, the adaptive Lasso including sample splitting, selective inference conditional on the Lasso selection, and debiased Lasso. Using a neutral simulation design motivated by characteristics commonly observed in biomedical time-to-event datasets, we evaluate the empirical properties of selective confidence intervals. The methods are additionally demonstrated using a real-world biomedical dataset.
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