Bayes linear kinematics in a dynamic Bayesian survival model

TL;DR

This paper introduces a Bayesian linear kinematic approach for survival analysis with time-dependent covariates, offering computational efficiency and interpretability, demonstrated on leukemia patient data.

Contribution

It extends Bayes linear methods to handle complex survival models with time-varying effects, avoiding intensive computations and resolving non-commutativity issues.

Findings

Efficient modeling of survival data with time-dependent covariates.

Elimination of non-commutativity problems in Bayesian linear kinematics.

Successful application to leukemia survival data.

Abstract

Bayes linear kinematics and Bayes linear Bayes graphical models provide an extension of Bayes linear methods so that full conditional updates may be combined with Bayes linear belief adjustment. In this paper we investigate the application of this approach to a more complicated problem: namely survival analysis with time-dependent covariate effects. We use a piecewise-constant hazard function with a prior in which covariate effects are correlated over time. The need for computationally intensive methods is avoided and the relatively simple structure facilitates interpretation. Our approach eliminates the problem of non-commutativity which was observed in earlier work by Gamerman. We apply the technique to data on survival times for leukemia patients.