Work

Post‐selection inference for the Cox model with interval‐censored data

Year: 2025

Type: article

Abstract: We develop a post-selection inference method for the Cox proportional hazards model with interval-censored data, which provides asymptotically valid p-values and confidence intervals conditional on the model selected by lasso. The method is based on a pivotal quantity that is shown to converge to a uniform distribution under local parameters. Our method involves estimation of the efficient information matrix, for which several approaches are proposed with proof of their consistency. Thorough simulation studies show that our method has satisfactory performance in samples of modest sizes. The utility of the method is illustrated via an application to an Alzheimer's disease study.

Source: Scandinavian Journal of Statistics

DOI: 10.1111/sjos.12768

Authors: Jianrui Zhang, Chenxi Li, Haolei Weng

Institution: Michigan State University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,500