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Direct modeling of regression effects for transition probabilities in the progressive illness–death model
Author(s) -
Azarang Leyla,
Scheike Thomas,
de UñaÁlvarez Jacobo
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7245
Subject(s) - covariate , censoring (clinical trials) , statistics , estimator , regression analysis , regression , estimating equations , markov chain , econometrics , proportional hazards model , negative binomial distribution , medicine , mathematics , poisson distribution
In this work, we present direct regression analysis for the transition probabilities in the possibly non‐Markov progressive illness–death model. The method is based on binomial regression, where the response is the indicator of the occupancy for the given state along time. Randomly weighted score equations that are able to remove the bias due to censoring are introduced. By solving these equations, one can estimate the possibly time‐varying regression coefficients, which have an immediate interpretation as covariate effects on the transition probabilities. The performance of the proposed estimator is investigated through simulations. We apply the method to data from the Registry of Systematic Lupus Erythematosus RELESSER, a multicenter registry created by the Spanish Society of Rheumatology. Specifically, we investigate the effect of age at Lupus diagnosis, sex, and ethnicity on the probability of damage and death along time. Copyright © 2017 John Wiley & Sons, Ltd.