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The Good, the Bad and the Fitting: A...

Antonio, Anna Liza Malazarte.

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The Good, the Bad and the Fitting: A Bayesian Hierarchical Model for Patient Preferences Elicited through Discrete Choice Experiments.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Good, the Bad and the Fitting: A Bayesian Hierarchical Model for Patient Preferences Elicited through Discrete Choice Experiments./
Author:	Antonio, Anna Liza Malazarte.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	164 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10260740
ISBN:	9781369667226

The Good, the Bad and the Fitting: A Bayesian Hierarchical Model for Patient Preferences Elicited through Discrete Choice Experiments.
Antonio, Anna Liza Malazarte.

The Good, the Bad and the Fitting: A Bayesian Hierarchical Model for Patient Preferences Elicited through Discrete Choice Experiments. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 164 p.

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (D.P.H.)--University of California, Los Angeles, 2017.

In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.

ISBN: 9781369667226Subjects--Topical Terms:

1002712
Biostatistics.

The Good, the Bad and the Fitting: A Bayesian Hierarchical Model for Patient Preferences Elicited through Discrete Choice Experiments.
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245 1 4 $a The Good, the Bad and the Fitting: A Bayesian Hierarchical Model for Patient Preferences Elicited through Discrete Choice Experiments.
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300 $a 164 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Advisers: Robert E. Weiss; Catherine M. Crespi.
502 $a Thesis (D.P.H.)--University of California, Los Angeles, 2017.
520 $a In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.
590 $a School code: 0031.
650 4 $a Biostatistics. $3 1002712
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710 2 $a University of California, Los Angeles. $b Biostatistics. $3 3280770
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792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10260740