| A complication when assessing quality of life data longitudinally is that in many trials a substantial percentage of patients die before completing all of the assessments. Furthermore, a patient's risk of dying might be predicted by current quality of life. This suggests jointly modeling quality of life and survival, and using this combined information to summarize the outcome. A popular method for measuring quality of life is with instruments which utilize multiple-item subscales, in which each item is scored on a Likert scale. The aim of this research is to address the complicated issues, such as death, present in analyzing multiple-item ordinal quality of life data in clinical trials while recognizing the psychometric properties of the quality of life instrument being used. Combining item response models and survival models with time-dependent covariates, where a latent variable process for quality of life determines the probability of selecting various options on quality of life items, and also serves as a time-dependent covariate in the survival model, accomplish this.; We implement this by using Markov chain Monte Carlo methods to obtain parameter estimates. Then we compute a summary measure, area-under-QOL-curve, to compare the efficacy of the treatments. Both parametric and semiparametric survival models have been used to model the survival time. Simulations are conducted to address the performance of the methods. The model assumptions and goodness of fit are assessed. The methods are illustrated with analysis of data from the Vesnarinone Trial of patients with severe heart failure, in which quality of life was assessed with the Minnesota Living with Heart Failure Questionnaire. |
