Wu, YingCook, Richard J.2018-03-132018-03-132017-06-15https://doi.org/10.1002/sim.7269http://hdl.handle.net/10012/13036This is the peer reviewed version of the following article: Wu, Y., and Cook, R. J. (2017) A two-phase model for chronic disease processes under intermittent inspection. Statist. Med., 36: 2016-2031. doi: 10.1002/sim.7269, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.7269/full. This article may be used for noncommercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.A model is developed for chronic diseases with an indolent phase that is followed by a phase with more active disease resulting in progression and damage. The time scales for the intensity functions for the active phase are more naturally based on the time since the start of the active phase, corresponding to a semi-Markov formulation. This two-phase model enables one to fit a separate regression model for the duration of the indolent phase and intensity-based models for the more active second phase. In cohort studies for which the disease status is only known at a series of clinical assessment times, transition times are interval-censored, which means the time origin for phase II is interval-censored. Weakly parametric models with piecewise constant baseline hazard and rate functions are specified, and an expectation-maximization algorithm is described for model fitting. Simulation studies examining the performance of the proposed model show good performance under maximum likelihood and two-stage estimation. An application to data from the motivating study of disease progression in psoriatic arthritis illustrates the procedure and identifies new human leukocyte antigens associated with the duration of the indolent phase.enexpectation-maximization algorithminterval-censoringrecurrent eventstwo-phase processtwo-stage estimationArticle