Post-Selection Inference for the Cox Model with Interval-Censored Data

TL;DR

This paper introduces a post-selection inference method for the Cox model with interval-censored data, enabling valid p-values and confidence intervals after model selection, with broad applicability and demonstrated effectiveness.

Contribution

It develops a novel inference approach for the Cox model with interval censoring, extending to other regression models, and proposes consistent estimation methods for the information matrix.

Findings

Method provides asymptotically valid p-values and confidence intervals.

Simulation studies show satisfactory performance in modest samples.

Application to Alzheimer's data demonstrates practical utility.

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 alternatives. The proof can be adapted to many other regression models, which is illustrated by the extension to generalized linear models and the Cox model with right-censored data. 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.