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

• Activity Number: 542
• Type: Contributed
• Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Zhigang Li* and H. Rob Frost and Lihui Zhao and Lei Liu and Kathleen Lyons and Huaihou Chen and Bernard Cole and David Currow and Marie Bakitas and Tor Tosteson
• Companies: and Dartmouth College and Northwestern University and Northwestern University and Dartmouth College and University of Florida and University of Vermont and Flinders University and University of Alabama at Birmingham and Dartmouth College
• Keywords: Joint modeling ; longitudinal data ; survival analysis ; terminal trend ; quality of life ; semiparametric

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 assess the model through simulation and application on a recently completed palliative care clinical trial.