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dc.contributor.authorZeng, Leilei
dc.contributor.authorCook, Richard J.
dc.contributor.authorLee, Jooyoung
dc.date.accessioned2020-03-11 17:24:41 (GMT)
dc.date.available2020-03-11 17:24:41 (GMT)
dc.date.issued2020-02-20
dc.identifier.urihttps://doi.org/10.1002/sim.8411
dc.identifier.urihttp://hdl.handle.net/10012/15692
dc.descriptionThis is the peer reviewed version of the following article: Leilei Zeng, Richard J. Cook and Jooyoung Lee, Multistate analysis from cross-sectional and auxiliary samples. Statistics in Medicine (2019), 39(4): 387–408 which has been published in final form at https://doi.org/10.1002/sim.8411.en
dc.description.abstractEpidemiological 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.en
dc.description.sponsorshipThis work was supported by the Natural Science and Engineering Research Council of Canada through grants RGPIN 115928 (LZ) and RGPIN 155849 (RJC) and the Canadian Institutes for Health Research through grant FRN 13887 (RJC). Richard Cook is a Tier I Canada Research Chair in Statistical Methods for Health Research.en
dc.language.isoenen
dc.publisherWileyen
dc.relation.ispartofseriesStatistics in Medicine;39(4)
dc.subjectauxiliary dataen
dc.subjectcross-sectional sampleen
dc.subjectintensity functionen
dc.subjectMarkov modelen
dc.subjectmultistage disease processen
dc.titleMultistate analysis from cross-sectional and auxiliary samplesen
dc.typeArticleen
uws.contributor.affiliation1Faculty of Mathematicsen
uws.contributor.affiliation2Statistics and Actuarial Scienceen
uws.typeOfResourceTexten
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen
uws.scholarLevelPost-Doctorateen


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