A joint QoL-Survival framework with debiased estimation under truncation by death

TL;DR

This paper introduces a joint modeling framework for quality-of-life and survival data that handles truncation by death, providing flexible, assumption-lean estimators that incorporate machine learning, with applications demonstrated through simulations and real data.

Contribution

It develops a novel semiparametric, joint modeling approach for QoL and survival that avoids extrapolation beyond death and integrates machine learning techniques.

Findings

The method accurately estimates joint QoL-survival distributions in simulations.

It effectively handles truncation by death in real-world datasets.

The approach is flexible and transparent, accommodating complex data structures.

Abstract

Evaluating quality-of-life (QoL) outcomes in populations with high mortality risk is complicated by truncation by death, since QoL is undefined for individuals who do not survive to the planned measurement time. We propose a framework that jointly models the distribution of QoL and survival without extrapolating QoL beyond death. Inspired by multistate formulations, we extend the joint characterization of binary health states and mortality to continuous QoL outcomes. Because treatment effects cannot be meaningfully summarized in a single one-dimensional estimand without strong assumptions, our approach simultaneously considers both survival and the joint distribution of QoL and survival with the latter conveniently displayed in a simplex. We develop assumption-lean, semiparametric estimators based on efficient influence functions, yielding flexible, root-n consistent estimators that accommodate machine-learning methods while making transparent the conditions these must satisfy. The proposed method is illustrated through simulation studies and two real-data applications.