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Updated: Jun 25, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Statistical inference after variable selection in Cox models: a neutral simulation study.

Lena Schemet¹, Sarah Friedrich-Welz^2,3

¹Department of Mathematics, University of Augsburg, Augsburg, Bavaria, 86159, Germany. lena.schemet@uni-a.de.

BMC Medical Research Methodology

|June 24, 2026

Summary

This summary is machine-generated.

Post-selection inference methods for biomedical time-to-event data, particularly after Lasso variable selection in Cox models, can be biased. This study evaluates techniques like sample splitting and debiased Lasso to improve inference accuracy.

Keywords:

Cox model Debiased Lasso Lasso Post-selection inference Survival analysis

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Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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Last Updated: Jun 25, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Biostatistics
Survival Analysis
Statistical Inference

Background:

Variable selection in biomedical time-to-event data is crucial but challenging.
Classical frequentist inference assumes fixed covariates, leading to bias with data-driven selection.
Right-censored survival data introduces additional uncertainty complicating inference.

Purpose of the Study:

To investigate inference procedures following variable selection using Lasso and adaptive Lasso in Cox models.
To evaluate methods addressing bias in post-selection inference for biomedical survival data.
To distinguish between selected-submodel and full-model inferential targets.

Main Methods:

Examined sample splitting, explicit conditioning on Lasso selection, and the debiased Lasso.
Assessed empirical coverage, interval width, power, and Type I error.
Conducted a simulation study with realistic covariate structures and censoring rates.

Main Results:

Performance of different post-selection inference methods varied based on inferential targets.
The study highlighted the impact of variable selection on the reliability of statistical inference.
Applied procedures to a public survival dataset to demonstrate practical behavior.

Conclusions:

Naive post-selection inference after Lasso in Cox models can yield biased results.
Careful selection and application of inference methods are necessary for accurate biomedical survival data analysis.
The debiased Lasso and other techniques offer potential improvements for reliable inference.