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Dynamic Prediction of Acute Graft-versus-Host-Disease with Longitudinal Biomarkers

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서명/저자사항Dynamic Prediction of Acute Graft-versus-Host-Disease with Longitudinal Biomarkers.
개인저자Li, Yumeng.
단체저자명University of Michigan. Biostatistics.
발행사항[S.l.]: University of Michigan., 2018.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2018.
형태사항133 p.
기본자료 저록Dissertation Abstracts International 79-12B(E).
Dissertation Abstract International
ISBN9780438126435
학위논문주기Thesis (Ph.D.)--University of Michigan, 2018.
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Thomas M. Braun.
요약This dissertation builds three prediction tools to dynamically predict the onset of acute graft-versus-host disease (aGVHD) with longitudinal biomarkers. Acute graft-versus-host disease is a complication for patients who have received allogeneic
요약Our first project introduces how to apply joint modeling with latent classes (JMLC) and landmark analysis to aGVHD data. In JMLC, we group all aGVHD-free patients into one latent class and define that class as the "cure" class. In landmark analy
요약In our second project, we describe how to execute dynamic prediction with the pattern mixture model, in which each patient is classified by his/her time-to-aGVHD, and patients in the same group share the same mean profile of biomarkers. The patt
요약In our third project, we incorporate censored cases to generalize the pattern mixture model in the second project. The simulation results demonstrate that this generalized pattern mixture model accurately estimates of the marginal pattern probab
요약In our fourth project, we explain the process of parametric bootstrap in selecting the number of latent classes in JMLC. Compared with the standard information-based criteria in model selection in JMLC, our parametric bootstrap likelihood ratio
일반주제명Biostatistics.
언어영어
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