Joint modelling of progression-free and overall survival and computation of correlation measures

TL;DR

This paper develops a flexible multistate model to jointly analyze progression-free and overall survival, providing new formulas and inference methods for dependence measures like Pearson's correlation, applicable in clinical trial analysis.

Contribution

It introduces a general, non-Markov multistate modeling framework for joint survival analysis without copula assumptions, enabling inference on dependence measures.

Findings

Closed-form formulas for joint survival distribution.

Statistical inference methods for Pearson's correlation.

Application to a large breast cancer trial.

Abstract

In this paper, we derive the joint distribution of progression-free and overall survival as a function of transition probabilities in a multistate model. No assumptions on copulae or latent event times are needed and the model is allowed to be non-Markov. From the joint distribution, statistics of interest can then be computed. As an example, we provide closed formulas and statistical inference for Pearson's correlation coefficient between progression-free and overall survival in a parametric framework. The example is inspired by recent approaches to quantify the dependence between progression-free survival, a common primary outcome in phase III trials in oncology, and overall survival. We complement these approaches by providing methods of statistical inference while at the same time working within a much more parsimonious modelling framework. Our approach is completely general and can be applied to other measures of dependence. We also discuss extensions to nonparametric inference. Our analytical results are illustrated using a large randomized clinical trial in breast cancer.