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

• URL: http://arxiv.org/abs/2602.07477v1
• Date: Sat, 07 Feb 2026 10:14:21 GMT
• Title: Statistical inference after variable selection in Cox models: A simulation study
• Authors: Lena Schemet, Sarah Friedrich-Welz,
• Abstract summary: We investigate several inference procedures applied after variable selection for the coefficients of the Lasso and its extension, the adaptive Lasso.<n>The methods considered include sample splitting, exact post-selection inference, and the debiased Lasso.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding [52.1068936424622]
  We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention.<n>In settings without selection bias or confounding, $E[Y|do(X)] = E[Y|X]$, which can be estimated using standard regression methods.<n>We propose a framework that incorporates both selection bias and confounding.
  arXiv  Detail & Related papers  (2025-03-26T13:43:37Z)
• Selective Nonparametric Regression via Testing [54.20569354303575]
  We develop an abstention procedure via testing the hypothesis on the value of the conditional variance at a given point. Unlike existing methods, the proposed one allows to account not only for the value of the variance itself but also for the uncertainty of the corresponding variance predictor.
  arXiv  Detail & Related papers  (2023-09-28T13:04:11Z)
• Nonlinear Permuted Granger Causality [0.6526824510982799]
  Granger causal inference is a contentious but widespread method used in fields ranging from economics to neuroscience. To allow for out-of-sample comparison, a measure of functional connectivity is explicitly defined using permutations of the covariate set. Performance of the permutation method is compared to penalized variable selection, naive replacement, and omission techniques via simulation.
  arXiv  Detail & Related papers  (2023-08-11T16:44:16Z)
• Selective inference using randomized group lasso estimators for general models [3.4034453928075865]
  The method includes the use of exponential family distributions, as well as quasi-likelihood modeling for overdispersed count data. A randomized group-regularized optimization problem is studied. Confidence regions for the regression parameters in the selected model take the form of Wald-type regions and are shown to have bounded volume.
  arXiv  Detail & Related papers  (2023-06-24T01:14:26Z)
• Variable selection for nonlinear Cox regression model via deep learning [0.0]
  We extend the recently developed deep learning-based variable selection model LassoNet to survival data. We apply the proposed methodology to analyze a real data set on diffuse large B-cell lymphoma.
  arXiv  Detail & Related papers  (2022-11-17T01:17:54Z)
• Bounding Counterfactuals under Selection Bias [60.55840896782637]
  We propose a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal.
  arXiv  Detail & Related papers  (2022-07-26T10:33:10Z)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• Calibration of prediction rules for life-time outcomes using prognostic Cox regression survival models and multiple imputations to account for missing predictor data with cross-validatory assessment [0.0]
  Methods are described to combine imputation with predictive calibration in survival modeling subject to censoring. Prediction-averaging appears to have superior statistical properties, especially smaller predictive variation, as opposed to a direct application of Rubin's rules.
  arXiv  Detail & Related papers  (2021-05-04T20:10:12Z)
• Double machine learning for sample selection models [0.12891210250935145]
  This paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. We make use of (a) Neyman-orthogonal, doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias.
  arXiv  Detail & Related papers  (2020-11-30T19:40:21Z)
• Tracking disease outbreaks from sparse data with Bayesian inference [55.82986443159948]
  The COVID-19 pandemic provides new motivation for estimating the empirical rate of transmission during an outbreak. Standard methods struggle to accommodate the partial observability and sparse data common at finer scales. We propose a Bayesian framework which accommodates partial observability in a principled manner.
  arXiv  Detail & Related papers  (2020-09-12T20:37:33Z)
• Stable Prediction via Leveraging Seed Variable [73.9770220107874]
  Previous machine learning methods might exploit subtly spurious correlations in training data induced by non-causal variables for prediction. We propose a conditional independence test based algorithm to separate causal variables with a seed variable as priori, and adopt them for stable prediction. Our algorithm outperforms state-of-the-art methods for stable prediction.
  arXiv  Detail & Related papers  (2020-06-09T06:56:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.