Analysis of palliative care studies with joint models for quality-of-life measures and survival.

Abstract

41 Background: In palliative care studies, the primary outcomes are often health related quality of life measures (HRLQ). Randomized trials and prospective cohorts typically recruit patients with advanced stage of disease and follow them until death or end of the study. During this time, HRLQ measures are collected at regular intervals. The typical analysis involves comparing the means of two or more intervention or exposure groups at specific times from entry into the study. An important feature of such studies is that, by design, some patients, but not all, are likely to die during the course of the study. For instance, in the Dartmouth ENABLE II study (Bakitas et al., 2009), 60% of patients died during the study. This feature affects the interpretation of the conventional analysis of palliative care trials and suggests the need for specialized methods of analysis. Methods: We have developed a “terminal decline model” for palliative care trials that, by jointly modeling the time until death and the HRQL measures, leads to flexible interpretation and efficient analysis of the trial data (Li, Tosteson, Bakitas, 2012). Importantly, it allows the estimation of the quality of life in the months preceding death, and specifically incorporates data for patients not dying during the study. At the same time, it permits the efficient estimation and interpretation of HRQL effects as measured in the conventional analysis as the difference in HRQL at specified times from enrollment conditional on being alive. Finally, it allows the estimation of the HRQL weighted survival or quality adjusted life years (QALY). Results: Based on the terminal decline model, survival distributions are shown affect the conventional estimates of HRQL trends in palliative care trials. A direct estimate for quality of life at specified times prior to death is provided. The methods are illustrated with ENABLE II data as an approach for improving the conduct of palliative care studies. Conclusions: Proper interpretation of palliative care studies requires consideration of the joint distribution of quality of life measures and survival as in the terminal decline model.