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Joint modelling of survival and long...

Ding, Jimin.

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Joint modelling of survival and longitudinal data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Joint modelling of survival and longitudinal data./
作者:	Ding, Jimin.
面頁冊數:	110 p.
附註:	Adviser: Jane-Ling Wang.
Contained By:	Dissertation Abstracts International67-09B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3235985
ISBN:	9780542898556

Joint modelling of survival and longitudinal data.
Ding, Jimin.

Joint modelling of survival and longitudinal data. - 110 p.

Adviser: Jane-Ling Wang.

Thesis (Ph.D.)--University of California, Davis, 2006.

Several approaches for the joint modelling of survival analysis and longitudinal (functional data) analysis are developed in this thesis.

ISBN: 9780542898556Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Joint modelling of survival and longitudinal data.
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100 1 $a Ding, Jimin. $3 1286962
245 1 0 $a Joint modelling of survival and longitudinal data.
300 $a 110 p.
500 $a Adviser: Jane-Ling Wang.
500 $a Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 5169.
502 $a Thesis (Ph.D.)--University of California, Davis, 2006.
520 $a Several approaches for the joint modelling of survival analysis and longitudinal (functional data) analysis are developed in this thesis.
520 $a In clinical studies, longitudinal covariates are often used to monitor the progression of a disease as well as survival time. Relationship between a failure time process and some longitudinal covariates is of key interest and so is the understanding of the pattern of longitudinal process to learn more about health status of patients, or to get some insight into the progression of disease. Joint modelling of longitudinal and survival data has certain advantages and emerged as an effective way to gain information from both components. Traditionally, a parametric longitudinal model is assumed to facilitate the likelihood approach. However, the choice of a proper parametric model turns out more illusive than standard longitudinal studies where no survival end-point occurs. Furthermore, the computational burden due to both Monte Carlo numerical integration and EM (Expectation Maximization) algorithm is an important concern in the joint modeling setting.
520 $a To deal with those challenges, we propose a parsimonious nonparametric longitudinal model in the joint modelling setting. The longitudinal process is represented by some basis functions and a proportional hazards model is then used to link these with the event-time. Unknown model parameters are estimated through maximizing the observed joint likelihood, which is iteratively maximized by the Monte Carlo Expectation Maximization (MCEM) algorithm. Simplicity of the model structure is crucial for good numerical stability, so the parsimonious nonparametric model is preferred for computational advantages and it compares well to competing parametric approaches. We also introduce the method of sieves for joint modelling to illustrate the high dimensionality problem currently encountered in the joint modelling literature. The consistency and rate of convergence of the proposed sieve estimator are established.
590 $a School code: 0029.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
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710 2 $a University of California, Davis. $3 1018682
773 0 $t Dissertation Abstracts International $g 67-09B.
790 $a 0029
790 1 0 $a Wang, Jane-Ling, $e advisor
791 $a Ph.D.
792 $a 2006
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