A competing risks approach for nonparametric estimation of transition probabilities in a non-Markov illness-death model

TL;DR

This paper introduces a modified competing risks model for nonparametric estimation of transition probabilities in illness-death models, relaxing the Markov assumption to account for history-dependent patient outcomes.

Contribution

It develops a new approach that allows nonparametric estimation of transition probabilities without relying on the Markov assumption in illness-death models.

Findings

The model accommodates non-Markovian processes in hospital epidemiology.

It provides a framework for more accurate patient outcome analysis.

The approach is demonstrated with relevant epidemiological data.

Abstract

Competing risks model time to first event and type of first event. An example from hospital epidemiology is the incidence of hospital-acquired infection, which has to account for hospital discharge of non-infected patients as a competing risk. An illness-death model would allow to further study hospital outcomes of infected patients. Such a model typically relies on a Markov assumption. However, it is conceivable that the future course of an infected patient does not only depend on the time since hospital admission and current infection status but also on the time since infection. We demonstrate how a modified competing risks model can be used for nonparametric estimation of transition probabilities when the Markov assumption is violated.