Multistate Analysis of Interval-Censored Longitudinal Data: Application to a Cohort Study on Performance Status Among Patients Diagnosed With Cancer

摘要

In observational studies on cancer patients, progression of performance status over time can be described bynusing a multistate model in which state-to-state transitions represent changes in a patient’s health condition.nAlthough a patient experiences transitions in continuous time, assessments on the patient are often made atnirregularly spaced time points. In this paper, the authors formulate a Markov 4-state model for examining longitu-ndinal data on performance status collected under intermittent observation. The cohort consisted of 11,342 patientsndiagnosed with cancer in Ontario, Canada, from 2007 to 2009. The authors extend the model to estimate thenpredicted probability of reaching the absorbing state, death, over various time intervals. The authors also illustratenwhat happens to the estimated transition intensities if the true observational scheme is overlooked. Methods fornmultistate analysis should be used by epidemiologists, since they prove particularly useful for examining thencomplexities of disease processes.