A two-phase model for chronic disease processes under intermittent inspection.

Abstract

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. Copyright © 2017 John Wiley & Sons, Ltd.

• Authors: Ying Wu, Richard J Cook
• Journal: Statistics in medicine (Stat Med) Vol. 36 Issue 13 Pg. 2016-2031 (06 15 2017) ISSN: 1097-0258 [Electronic] England
• PMID: 28238209 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2017 John Wiley & Sons, Ltd.

Chemical References

• HLA Antigens

Topics

• Algorithms
• Arthritis, Psoriatic (immunology, pathology)
• Chronic Disease (epidemiology)
• Disease Progression
• HLA Antigens (immunology)
• Humans
• Likelihood Functions
• Markov Chains
• Models, Statistical
• Time Factors