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Nonparametric methods for multivaria...

Dubin, Joel Alan.

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Nonparametric methods for multivariate longitudinal data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Nonparametric methods for multivariate longitudinal data./
Author:	Dubin, Joel Alan.
Description:	71 p.
Notes:	Adviser: Hans-Georg Muller.
Contained By:	Dissertation Abstracts International61-09B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9987452
ISBN:	0599942843

Nonparametric methods for multivariate longitudinal data.
Dubin, Joel Alan.

Nonparametric methods for multivariate longitudinal data. - 71 p.

Adviser: Hans-Georg Muller.

Thesis (Ph.D.)--University of California, Davis, 2000.

Methods for analyzing longitudinal data are prevalent in the statistics and epidemiology literature. However, the vast majority of these methods focus on a univariate longitudinal response. We propose a nonparametric method to quantify and describe the dynamical correlation for a collection of multivariate longitudinal responses, including consideration of lags and derivatives. We then propose a related method to determine if a multivariate set of longitudinal variables can explain a single longitudinal variable, utilizing a regression approach. For both methods, we assume each of the longitudinal variables can be described as a realization of a smooth stochastic process. The proposed methods can be utilized when observation times are irregular and not matching between subjects or between responses within subject. We demonstrate the methods with data on five acute phase blood proteins measured longitudinally for a sample of hemodialysis patients.

ISBN: 0599942843Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Nonparametric methods for multivariate longitudinal data.
LDR:02823nam 2200289 a 45 001 929303
005 20110427
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020 $a 0599942843
035 $a (UnM)AAI9987452
035 $a AAI9987452
040 $a UnM $c UnM
100 1 $a Dubin, Joel Alan. $3 1252787
245 1 0 $a Nonparametric methods for multivariate longitudinal data.
300 $a 71 p.
500 $a Adviser: Hans-Georg Muller.
500 $a Source: Dissertation Abstracts International, Volume: 61-09, Section: B, page: 4809.
502 $a Thesis (Ph.D.)--University of California, Davis, 2000.
520 $a Methods for analyzing longitudinal data are prevalent in the statistics and epidemiology literature. However, the vast majority of these methods focus on a univariate longitudinal response. We propose a nonparametric method to quantify and describe the dynamical correlation for a collection of multivariate longitudinal responses, including consideration of lags and derivatives. We then propose a related method to determine if a multivariate set of longitudinal variables can explain a single longitudinal variable, utilizing a regression approach. For both methods, we assume each of the longitudinal variables can be described as a realization of a smooth stochastic process. The proposed methods can be utilized when observation times are irregular and not matching between subjects or between responses within subject. We demonstrate the methods with data on five acute phase blood proteins measured longitudinally for a sample of hemodialysis patients.
520 $a Lastly, in the case where one or a group of longitudinal variables may play a role in the survival experience for a specified cohort, we present a compact nonparametric graphical method that conveys survival information, censoring information, and time-varying covariate influence on survival. The proposed graph contains the Kaplan-Meier estimator for right-censored data and a simultaneous display of the behavior of time-dependent covariate(s) and the lifetime for each subject in the sample. The observed levels of time-dependent covariates are possibly subjected to an initial dimension reduction or smoothing step to produce a continuous covariate function. Values of this function are plotted on a horizontal bar for the length of the lifetime of the subject. Covariate information for censored data is also incorporated. The union of the horizontal bars forms the Kaplan-Meier estimator of the survival function. This method is applied to several biomedical datasets.
590 $a School code: 0029.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0463
710 2 0 $a University of California, Davis. $3 1018682
773 0 $t Dissertation Abstracts International $g 61-09B.
790 $a 0029
790 1 0 $a Muller, Hans-Georg, $e advisor
791 $a Ph.D.
792 $a 2000
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9987452