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Bayesian inference for correlated bi...

Carlin, Patricia M.

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Bayesian inference for correlated binary data with an application to diabetes complication progression.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian inference for correlated binary data with an application to diabetes complication progression./
Author:	Carlin, Patricia M.
Description:	104 p.
Notes:	Adviser: John W. Seaman, Jr.
Contained By:	Dissertation Abstracts International67-04B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3216367
ISBN:	9780542662348

Bayesian inference for correlated binary data with an application to diabetes complication progression.
Carlin, Patricia M.

Bayesian inference for correlated binary data with an application to diabetes complication progression. - 104 p.

Adviser: John W. Seaman, Jr.

Thesis (Ph.D.)--Baylor University, 2006.

Correlated binary measurements can occur in a variety of practical contexts and afford interesting statistical modeling challenges. In order to model the separate probabilities for each measurement we must somehow account for the relationship between them. We choose to focus our applications to the progression of the complications of diabetic retinopathy and diabetic nephropathy.

ISBN: 9780542662348Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Bayesian inference for correlated binary data with an application to diabetes complication progression.
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035 $a (UnM)AAI3216367
035 $a AAI3216367
040 $a UnM $c UnM
100 1 $a Carlin, Patricia M. $3 1292869
245 1 0 $a Bayesian inference for correlated binary data with an application to diabetes complication progression.
300 $a 104 p.
500 $a Adviser: John W. Seaman, Jr.
500 $a Source: Dissertation Abstracts International, Volume: 67-04, Section: B, page: 2061.
502 $a Thesis (Ph.D.)--Baylor University, 2006.
520 $a Correlated binary measurements can occur in a variety of practical contexts and afford interesting statistical modeling challenges. In order to model the separate probabilities for each measurement we must somehow account for the relationship between them. We choose to focus our applications to the progression of the complications of diabetic retinopathy and diabetic nephropathy.
520 $a We first consider probabilistic models which employ Bayes' theorem for predicting the probability of onset of diabetic nephropathy given that a patient has developed diabetic retinopathy, modifying the work of Ballone, Colagrande, Di Nicola, Di Mascio, Di Mascio, and Capani (2003). We consider beta-binomial models using the Sarmanov (1966) framework which allows us to specify the marginal distributions for a given bivariate likelihood. We present both maximum likelihood and Bayesian methods based on this approach. Our Bayesian methods include a fully identified model based on proportional probabilities of disease incidence.
520 $a Finally, we consider Bayesian models for three different prior structures using likelihoods representing the data in the form of a 2 x 2 table. To do so, we consider the data as counts resulting from two correlated binary measurements: the onset of diabetic retinopathy and the onset of diabetic nephropathy. We compare resulting posterior distributions from a Jeffreys' prior, independent beta priors, and conditional beta priors, based on a structural zero likelihood model and the bivariate binomial model.
590 $a School code: 0014.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
650 4 $a Health Sciences, Public Health. $3 1017659
690 $a 0308
690 $a 0463
690 $a 0573
710 2 0 $a Baylor University. $3 771397
773 0 $t Dissertation Abstracts International $g 67-04B.
790 $a 0014
790 1 0 $a Seaman, John W., Jr., $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3216367