Application of Cox Model to predict the survival of patients with Chronic Heart Failure: A latent class regression approach

TL;DR

This study introduces a latent class Cox model to improve survival predictions for chronic heart failure patients by identifying subgroups and customizing risk models, outperforming standard approaches.

Contribution

It presents a novel latent class Cox model that enhances survival prediction accuracy by modeling patient subgroups with distinct risk profiles.

Findings

Latent class Cox model outperforms standard Cox model in predictive accuracy.

Model identifies meaningful patient subgroups with different risk levels.

Improved discrimination shown through higher AUC scores.

Abstract

Most prediction models that are used in medical research fail to accurately predict health outcomes due to methodological limitations. Using routinely collected patient data, we explore the use of a Cox proportional hazard (PH) model within a latent class framework to model survival of patients with chronic heart failure (CHF). We identify subgroups of patients based on their risk with the aid of available covariates. We allow each subgroup to have its own risk model.We choose an optimum number of classes based on the reported Bayesian information criteria (BIC). We assess the discriminative ability of the chosen model using an area under the receiver operating characteristic curve (AUC) for all the cross-validated and bootstrapped samples.We conduct a simulation study to compare the predictive performance of our models. Our proposed latent class model outperforms the standard one class Cox PH model.