A Semiparametric Joint Model for Terminal Trend of Quality of Life and Survival in Palliative Care Research

TL;DR

This paper introduces a novel semiparametric joint modeling approach for analyzing the correlated longitudinal quality of life and survival data in palliative care trials, addressing challenges posed by decreasing QOL and censoring.

Contribution

It develops a combined semiparametric mixed effects and Cox model using regression splines to better analyze QOL and survival data in palliative care research.

Findings

Model performs well in simulations

Application demonstrates practical utility

Provides a flexible framework for future trials

Abstract

Palliative medicine is an interdisciplinary specialty focusing on improving quality of life (QOL) for patients with serious illness and their families. Palliative care programs are available or under development at over 80% of large US hospitals (300+ beds). Palliative care clinical trials present unique analytic challenges relative to evaluating the palliative care treatment efficacy which is to improve patients diminishing QOL as disease progresses towards end of life (EOL). A unique feature of palliative care clinical trials is that patients will experience decreasing QOL during the trial despite potentially beneficial treatment. Often longitudinal QOL and survival data are highly correlated which, in the face of censoring, makes it challenging to properly analyze and interpret longitudinal QOL trajectory. To address these issues, we propose a novel semiparametric statistical approach to jointly model longitudinal QOL and survival data. There are two sub-models in our approach: a semiparametric mixed effects model for longitudinal QOL and a Cox model for survival. We use regression splines method to estimate the nonparametric curves and AIC to select knots. We assess the model through simulation and application to establish a novel modeling approach that could be applied in future palliative care treatment research trials.