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Statistical Methods for Dealing with Outcome Misclassification in Studies with Competing Risks Survival Outcomes

Indiana University-Purdue University Indianapolis (IUPUI) / In studies with competing risks outcomes, misidentifying the event-type responsible
for the observed failure is, by definition, an act of misclassification. Several authors have
established that such misclassification can bias competing risks statistical analyses, and have
proposed statistical remedies to aid correct modeling. Generally, these rely on adjusting
the estimation process using information about outcome misclassification, but invariably
assume that outcome misclassification is non-differential among study subjects regardless
of their individual characteristics. In addition, current methods tend to adjust for the
misclassification within a semi-parametric framework of modeling competing risks data.
Building on the existing literature, in this dissertation, we explore the parametric modeling
of competing risks data in the presence of outcome misclassification, be it differential or
non-differential. Specifically, we develop parametric pseudo-likelihood-based approaches
for modeling cause-specific hazards while adjusting for misclassification information that is
obtained either through data internal or external to the current study (respectively, internal
or external-validation sampling). Data from either type of validation sampling are used
to model predictive values or misclassification probabilities, which, in turn, are used to
adjust the cause-specific hazard models. We show that the resulting pseudo-likelihood
estimates are consistent and asymptotically normal, and verify these theoretical properties
using simulation studies. Lastly, we illustrate the proposed methods using data from a
study involving people living with HIV/AIDS (PLWH)in the East-African consortium of the International Epidemiologic Databases for the Evaluation of HIV/AIDS (IeDEA EA). In
this example, death is frequently misclassified as disengagement from care as many deaths
go unreported to health facilities caring for these patients. In this application, we model
the cause-specific hazards of death and disengagement from care among PLWH after they
initiate anti-retroviral treatment, while adjusting for death misclassification. / 2021-03-10

Identiferoai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/22280
Date02 1900
CreatorsMpofu, Philani Brian
ContributorsYiannoutsos, Constantin, Bakoyannis, Giorgios, Tu, Wanzhu, Song, Yiqing
Source SetsIndiana University-Purdue University Indianapolis
Languageen_US
Detected LanguageEnglish
TypeDissertation

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