Dynamic survival analysis: modelling the hazard function via ordinary differential equations

TL;DR

This paper introduces a novel framework for modeling the hazard function in survival analysis using autonomous ordinary differential equations, enabling both qualitative and quantitative insights into hazard dynamics.

Contribution

It presents a parametric approach to model hazard function dynamics with ODEs, integrating Bayesian methods and providing practical implementation strategies.

Findings

Effective modeling of hazard dynamics demonstrated through simulations.

Real data case studies show interpretability and applicability.

Framework adaptable to include covariates and extend to various contexts.

Abstract

The hazard function represents one of the main quantities of interest in the analysis of survival data. We propose a general approach for parametrically modelling the dynamics of the hazard function using systems of autonomous ordinary differential equations (ODEs). This modelling approach can be used to provide qualitative and quantitative analyses of the evolution of the hazard function over time. Our proposal capitalises on the extensive literature of ODEs which, in particular, allow for establishing basic rules or laws on the dynamics of the hazard function via the use of autonomous ODEs. We show how to implement the proposed modelling framework in cases where there is an analytic solution to the system of ODEs or where an ODE solver is required to obtain a numerical solution. We focus on the use of a Bayesian modelling approach, but the proposed methodology can also be coupled with maximum likelihood estimation. A simulation study is presented to illustrate the performance of these models and the interplay of sample size and censoring. Two case studies using real data are presented to illustrate the use of the proposed approach and to highlight the interpretability of the corresponding models. We conclude with a discussion on potential extensions of our work and strategies to include covariates into our framework. Although we focus on examples on Medical Statistics, the proposed framework is applicable in any context where the interest lies on estimating and interpreting the dynamics hazard function.