| LDR | | 02600nam u200421 4500 |
| 001 | | 000000419073 |
| 005 | | 20190215163402 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438126435 |
| 035 | |
▼a (MiAaPQ)AAI10903037 |
| 035 | |
▼a (MiAaPQ)umichrackham:001296 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 574 |
| 100 | 1 |
▼a Li, Yumeng. |
| 245 | 10 |
▼a Dynamic Prediction of Acute Graft-versus-Host-Disease with Longitudinal Biomarkers. |
| 260 | |
▼a [S.l.]:
▼b University of Michigan.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 133 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Thomas M. Braun. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Michigan, 2018. |
| 520 | |
▼a This 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 |
| 520 | |
▼a Our 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 |
| 520 | |
▼a In 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 |
| 520 | |
▼a In 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 |
| 520 | |
▼a In 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 |
| 590 | |
▼a School code: 0127. |
| 650 | 4 |
▼a Biostatistics. |
| 690 | |
▼a 0308 |
| 710 | 20 |
▼a University of Michigan.
▼b Biostatistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0127 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000537
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |