Jiang, ShuCook, Richard J.2021-01-132021-01-132019-07-20https://doi.org/10.1002/sim.8155http://hdl.handle.net/10012/16638This is the peer reviewed version of the following article: Shu Jiang and Richard J. Cook, Score tests based on a finite mixture model of Markov processes under intermittent observation, Statistics in Medicine (2019), 38(16): 3013–3025 which has been published in final form at https://doi.org/10.1002/sim.8155. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.A mixture model is described, which accommodates different Markov processes governing disease progression in a finite set of latent classes. We give special attention to the setting in which individuals are examined intermittently and transition times are consequently interval censored. A score test is developed to identify genetic markers associated with class membership. Simulation studies are conducted to validate the algorithm, assess the finite sample properties of the estimators, and assess the frequency properties of the score tests. A permutation test is recommended for settings when there is concern that the asymptotic approximation to the score test is poor. An application involving progression in joint damage in psoriatic arthritis (PsA) provides illustration and identifies human leukocyte antigen markers associated with unilateral and bilateral sacroiliac damage in individuals with PsA.enfinite mixture modelintermittent observationMarkov processmultistate modelscore testArticle