title: Multistate analysis of interval-censored longitudinal data: application to a cohort study on performance status among patients diagnosed with cancer.
abstract: In observational studies on cancer patients, progression of performance status over time can be described by using a multistate model in which state-to-state transitions represent changes in a patient's health condition. Although a patient experiences transitions in continuous time, assessments on the patient are often made at irregularly spaced time points. In this paper, the authors formulate a Markov 4-state model for examining longitudinal data on performance status collected under intermittent observation. The cohort consisted of 11,342 patients diagnosed with cancer in Ontario, Canada, from 2007 to 2009. The authors extend the model to estimate the predicted probability of reaching the absorbing state, death, over various time intervals. The authors also illustrate what happens to the estimated transition intensities if the true observational scheme is overlooked. Methods for multistate analysis should be used by epidemiologists, since they prove particularly useful for examining the complexities of disease processes.
authors.affiliation: Institute for Clinical Evaluative Sciences, Dalla Lana School of Public Health, University of Toronto, Ontario, Canada. rinku.sutradhar@ices.on.ca
authors.forename: Rinku
authors.initials: R
authors.lastname: Sutradhar
authors.forename: Lisa
authors.initials: L
authors.lastname: Barbera
authors.forename: Hsien
authors.initials: H
authors.lastname: Seow
authors.forename: Doris
authors.initials: D
authors.lastname: Howell
authors.forename: Amna
authors.initials: A
authors.lastname: Husain
authors.forename: Deborah
authors.initials: D
authors.lastname: Dudgeon
mesh_terms: D004185:Disability Evaluation; D018450:Disease Progression; D006305:Health Status Indicators; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D008962:Models, Theoretical; D009369:Neoplasms / Q000453:epidemiology*; D019370:Observation; D009864:Ontario / Q000453:epidemiology; D011237:Predictive Value of Tests; D011379:Prognosis; D011788:Quality of Life; D012042:Registries; D016019:Survival Analysis