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Most prediction models that are used in medical research fail to accuratelypredict health outcomes due to methodological limitations. Using routinelycollected patient data, we explore the use of a Cox proportional hazard (PH)model within a latent class framework to model survival of patients withchronic heart failure (CHF). We identify subgroups of patients based on theirrisk with the aid of available covariates. We allow each subgroup to have itsown risk model.We choose an optimum number of classes based on the reportedBayesian information criteria (BIC). We assess the discriminative ability ofthe chosen model using an area under the receiver operating characteristiccurve (AUC) for all the cross-validated and bootstrapped samples.We conduct asimulation study to compare the predictive performance of our models. Ourproposed latent class model outperforms the standard one class Cox PH model.

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