| LDR | | 03140nam u200445 4500 |
| 001 | | 000000419112 |
| 005 | | 20190215163421 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438127227 |
| 035 | |
▼a (MiAaPQ)AAI10903117 |
| 035 | |
▼a (MiAaPQ)umichrackham:001227 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 574 |
| 100 | 1 |
▼a Beesley, Lauren J. |
| 245 | 10 |
▼a Missing Data and Variable Selection Methods for Cure Models in Cancer Research. |
| 260 | |
▼a [S.l.]:
▼b University of Michigan.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 301 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Jeremy Michael George Taylor. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Michigan, 2018. |
| 520 | |
▼a In survival analysis, a common assumption is that all subjects will eventually experience the event of interest given long enough follow-up time. However, there are many settings in which this assumption does not hold. For example, suppose we ar |
| 520 | |
▼a The Cox proportional hazards (CPH) mixture cure model and a generalization, the multistate cure model, can be used to model time-to-event outcomes in the cure setting. In this dissertation, we will address issues of missing data, variable select |
| 520 | |
▼a In Chapter II, we propose several chained equations methods for imputing missing covariates under the CPH mixture cure model, and we compare the novel approaches with existing chained equations methods for imputing survival data without a cured |
| 520 | |
▼a In Chapter III, we develop sequential imputation methods for a general class of models with latent and partially latent variables (of which cure models are an example). In particular, we consider the setting where covariate/outcome missingness d |
| 520 | |
▼a In Chapter IV, we develop an EM algorithm for fitting the multistate cure model. The existing method for fitting this model requires custom software and can be slow to converge. In contrast, the proposed method can be easily implemented using st |
| 520 | |
▼a In Chapter V, we propose a generalization of the multistate cure model to incorporate subjects with persistent disease. This model has many parameters, and variable selection/shrinkage methods are needed to aid in estimation. We compare the perf |
| 520 | |
▼a In Chapter VI, we develop Bayesian methods for performing variable selection when we have order restrictions for model parameters. In particular, we consider the setting in which we have interactions with one or more order-restricted variables. |
| 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=T15000609
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |