Assessing survival models by interval testing

TL;DR

This paper introduces a set of statistical methods to evaluate and select the most appropriate parametric survival models in health economic evaluations, helping identify poor fits and guiding model rejection decisions.

Contribution

It presents novel interval testing methods and visualization tools for assessing the fit of survival models, improving model selection accuracy.

Findings

Methods effectively identify poor model fits.

Plots and p-values guide model rejection.

Enhances decision-making in health economic evaluations.

Abstract

When considering many survival models, decisions become more challenging in health economic evaluation. In this paper, we present a set of methods to assist with selecting the most appropriate parametric survival models. The methods highlight areas of particularly poor fit. Furthermore, plots and overall p-values provide guidance on whether a parametric survival model should be rejected or not.