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.

Key Words:

• Interval censoring
• Markov process
• Quality of life
• Multistate survival analysis

Mathematics Subject Classification:

• Primary 62N02
• Secondary 60J20

Acknowledgments

The authors would like to thank two anonymous referees for their most critical reading of the manuscript and useful comments and suggestions. Support for this study was supported by the UNC Center for AIDS Research (CFAR), an NIH funded program P30 A150410 and partially provided by Family Health International (FHI) with funds from the United States Agency for International Development (USAID). The views expressed in this article do not necessarily reflect those of FHI or USAID.