Variable Selection using Inverse Survival Probability Weighting

TL;DR

This paper introduces two novel variable selection methods using inverse survival probability weighting to improve survival time analysis, demonstrating their effectiveness through simulations and clinical data.

Contribution

It proposes ISPW-based lasso and information criterion methods for variable selection in censored survival data, with proven consistency and demonstrated practical performance.

Findings

Both methods show high estimation accuracy.

They achieve consistent variable selection.

Effective in clinical survival data analysis.

Abstract

In this paper, we propose two variable selection methods for adjusting the censoring information for survival times, such as the restricted mean survival time. To adjust for the influence of censoring, we consider an inverse survival probability weighting (ISPW) for subjects with events. We derive a least absolute shrinkage and selection operator (lasso)-type variable selection method, which considers an inverse weighting for of the squared losses, and an information criterion-type variable selection method, which applies an inverse weighting of the survival probability to the power of each density function in the likelihood function. We prove the consistency of the ISPW lasso estimator and the maximum ISPW likelihood estimator. The performance of the ISPW lasso and ISPW information criterion are evaluated via a simulation study with six scenarios, and then their variable selection ability is demonstrated using data from two clinical studies. The results confirm that ISPW lasso and the ISPW likelihood function produce good estimation accuracy and consistent variable selection. We conclude that our two proposed methods are useful variable selection tools for adjusting the censoring information for survival time analyses.