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Multistate analysis from cross‐sectional and auxiliary samples
Author(s) -
Zeng Leilei,
Cook Richard J.,
Lee Jooyoung
Publication year - 2019
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.8411
Subject(s) - estimator , logistic regression , cross sectional study , statistics , econometrics , population , sample (material) , regression , regression analysis , epidemiology , sample size determination , computer science , medicine , mathematics , demography , environmental health , chemistry , chromatography , sociology
Epidemiological studies routinely involve cross‐sectional sampling of a population comprised of individuals progressing through life history processes. We consider features of a cross‐sectional sample in terms of the intensity functions of a progressive multistate disease process under stationarity assumptions. The limiting values of estimators for regression coefficients in naive logistic regression models are studied, and simulations confirm the key asymptotic results that are relevant in finite samples. We also consider the need for and the use of data from auxiliary samples, which enable one to fit the full multistate life history process. We conclude with an application to data from a national cross‐sectional sample assessing marker effects on psoriatic arthritis among individuals with psoriasis.