Cross-validatory Z-Residual for Diagnosing Shared Frailty Models

TL;DR

This paper introduces a cross-validatory method for computing Z-residuals in shared frailty survival models, improving diagnostic power for model fit and outlier detection over traditional residuals.

Contribution

It develops a general function for cross-validatory Z-residuals in shared frailty models, enhancing residual diagnostics with better power and discrimination.

Findings

Cross-validatory Z-residuals outperform traditional Z-residuals in simulation tests.

Cross-validatory Z-residuals more effectively detect outliers in real data.

Proposed method is computationally feasible with existing R tools.

Abstract

Residual diagnostic methods play a critical role in assessing model assumptions and detecting outliers in statistical modelling. In the context of survival models with censored observations, Li et al. (2021) introduced the Z-residual, which follows an approximately normal distribution under the true model. This property makes it possible to use Z-residuals for diagnosing survival models in a way similar to how Pearson residuals are used in normal regression. However, computing residuals based on the full dataset can result in a conservative bias that reduces the power of detecting model mis-specification, as the same dataset is used for both model fitting and validation. Although cross-validation is a potential solution to this problem, it has not been commonly used in residual diagnostics due to computational challenges. In this paper, we propose a cross-validation approach for computing Z-residuals in the context of shared frailty models. Specifically, we develop a general function that calculates cross-validatory Z-residuals using the output from the \texttt{coxph} function in the \texttt{survival} package in R.Our simulation studies demonstrate that, for goodness-of-fit tests and outlier detection, cross-validatory Z-residuals are significantly more powerful and more discriminative than Z-residuals without cross-validation. We also compare the performance of Z-residuals with and without cross-validation in identifying outliers in a real application that models the recurrence time of kidney infection patients. Our findings suggest that cross-validatory Z-residuals can identify outliers that are missed by Z-residuals without cross-validation.