arXiv subjects

Lena Schemet

Publications and source records attributed to Lena Schemet.

4 recordsLinked to original sources

arXiv2026

Selection instability and coefficient uncertainty in Cox modelling after Lasso selection

After variable selection, uncertainty quantification often treats the selected model as fixed, although it may change under repeated sampling. We investigate whether this variability can be incorporated by repeating Cox-Lasso selection within a model-based bootstrap after unpenalised Cox refitting. We compare conventional post-selection Cox Wald intervals and a model-based bootstrap without re-selection, both of which keep the selected predictor set fixed, with two re-selection procedures: one retains the observed tuning-parameter value, whereas the other reapplies the cross-validation minimum (CV-min) tuning rule within each bootstrap sample. Against the data-generating coefficients, procedures holding the selected predictor set fixed frequently showed substantial undercoverage. Re-selection generally moved coverage closer to the nominal level, although performance varied across simulation regimes and coefficients. Sensitivity analyses evaluating coverage against the population Cox coefficients corresponding to each originally selected predictor set gave similar aggregate coverage for the primary interval comparison. In the NKI70 breast-cancer data, the four selected genes appeared together in only 0.6% of bootstrap samples under re-selection at the observed tuning-parameter value. These results suggest that selection instability can be an important component of post-selection coefficient uncertainty and that bootstrap re-selection can help incorporate this variability.

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arXiv2026

Wild Bootstrap and Efron's Bootstrap for Debiased Cox Regression

Cox regression with Lasso penalization is widely used for variable selection in time-to-event data, but reliable coefficient inference after selection remains difficult. We investigate bootstrap inference for the debiased Cox estimator after Cox Lasso selection. Two score-based procedures are considered: a wild bootstrap using independent multipliers and an Efron bootstrap using centered multinomial resampling weights. Both procedures keep the original Cox Lasso fit and debiasing matrix fixed and apply bootstrap weights to the score contributions that determine the first-order distribution of the debiased estimator. We establish asymptotic validity for both bootstrap schemes and study their finite-sample behavior in extensive simulations. The bootstrap intervals improve coverage accuracy over first-order debiased Wald intervals in many small- and moderate-sample settings, with the size of the improvement depending on the simulation setting and tuning rule. A SUPPORT2 application illustrates the procedures in practice.

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arXiv2026

Model-based bootstrap inference for Cox models after Lasso selection

Inference after variable selection in Cox regression is difficult because simple Wald-type intervals after selection can have poor finite-sample conditional coverage. We study a model-based bootstrap for inference after Cox-Lasso variable selection. The Cox-Lasso is fitted once to the original data to select a set of variables, after which an unpenalized Cox model is fitted using only those variables. Bootstrap samples are generated from a semiparametric plug-in Cox model specified by the coefficient estimate from this unpenalized Cox refit, the Breslow baseline cumulative hazard estimator, and a plug-in censoring distribution. In every bootstrap sample, the selected variable set is kept fixed and only the unpenalized Cox model is refitted. Under oracle-type sparse-model assumptions and standard Cox model regularity conditions, we prove first-order bootstrap validity for this procedure. In the simulation scenarios considered, percentile and studentized bootstrap intervals showed improved conditional coverage relative to the bootstrap-Wald interval in several small- and moderate-sample settings. Their performance was broadly competitive with debiased intervals, although the comparison depended on signal strength, tuning, and selection stability. A SEER breast cancer example illustrates that the procedure can be implemented in a realistic survival analysis and provides interpretable uncertainty quantification for effects reported after variable selection.

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arXiv2026

Statistical inference after variable selection in Cox models: A simulation study

Choosing relevant predictors is central to the analysis of biomedical time-to-event data. Classical frequentist inference, however, presumes that the set of covariates is fixed in advance and does not account for data-driven variable selection. As a consequence, naive post-selection inference may be biased and misleading. In right-censored survival settings, these issues may be further exacerbated by the additional uncertainty induced by censoring. We investigate several inference procedures applied after variable selection for the coefficients of the Lasso and its extension, the adaptive Lasso, in the context of the Cox model. The methods considered include sample splitting, exact post-selection inference, and the debiased Lasso. Their performance is examined in a neutral simulation study reflecting realistic covariate structures and censoring rates commonly encountered in biomedical applications. To complement the simulation results, we illustrate the practical behavior of these procedures in an applied example using a publicly available survival dataset.

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