A multistate Markov chain model for longitudinal, categorical quality-of-life data subject to non-ignorable missingness.
Quality-of-life (QOL) is an important outcome in clinical research, particularly in cancer clinical trials. Typically, data are collected longitudinally from patients during treatment and subsequent follow-up. Missing data are a common problem, and missingness may arise in a non-ignorable fashion. In particular, the probability that a patient misses an assessment may depend on the patient's QOL at the time of the scheduled assessment. We propose a Markov chain model for the analysis of categorical outcomes derived from QOL measures. Our model assumes that transitions between QOL states depend on covariates through generalized logit models or proportional odds models. To account for non-ignorable missingness, we incorporate logistic regression models for the conditional probabilities of observing measurements, given their actual values. The model can accommodate time-dependent covariates. Estimation is by maximum likelihood, summing over all possible values of the missing measurements. We describe options for selecting parsimonious models, and we study the finite-sample properties of the estimators by simulation. We apply the techniques to data from a breast cancer clinical trial in which QOL assessments were made longitudinally, and in which missing data frequently arose.
Cole
Bernard F
BF
Department of Community and Family Medicine, Section of Biostatistics and Epidemiology, Dartmouth College Medical School, Lebanon, NH 03756, USA. bernard.cole@darmouth.edu
Bonetti
Marco
M
Zaslavsky
Alan M
AM
Gelber
Richard D
RD
D000970:Antineoplastic Agents / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy / Q000523:psychology; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008390:Markov Chains; D008875:Middle Aged; D008960:Models, Psychological; D015233:Models, Statistical; D011788:Quality of Life
D000970:Antineoplastic Agents