Quality-Adjusted Survival Estimation with Periodic Observations: A Multistate Survival Analysis Approach

Abstract

Quality-adjusted survival is a composite measure that combines quality-of-life and survival data. Analyzing quality-adjusted survival with data collected at periodic intervals can be difficult because of incomplete information resulting from dropouts or missing visits. Dropouts could be purely random or caused by treatments or the illness itself. In a multistate model, dropout information can be incorporated into analysis by including the “dropout” state as a state of the patient's health. Under a Markovian assumption on patients' health status, we applied a multistate survival analysis approach that extends Chen and Sen's study [Chen, P.-L., Sen, P. K. (2001). Quality adjusted survival estimation with periodic observation. Biometrics 57:868–874] which estimated the mean quality-adjusted survival where the transition probability between health states and patients' expected survival time can be estimated simultaneously. Here we show that the estimator is asymptotically normal with simple variance calculation. We conducted a simulation study to investigate the behavior of the estimator, and used a long-term contraceptive study to illustrate the use of the estimator.