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Nonparametric Estimation of Latent Treatment Effects for Two Armed Clinical Trials Using Infinite Mixture Models /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonparametric Estimation of Latent Treatment Effects for Two Armed Clinical Trials Using Infinite Mixture Models /Benjamin Jefferson Ellis.
Author:	Ellis, Benjamin Jefferson,
Description:	1 electronic resource (212 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
Notes:	Advisors: Jeske, Daniel R. Committee members: Kurum, Esra; Yao, Weixin.
Contained By:	Dissertations Abstracts International87-01B.
Subject:	Statistics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32045467
ISBN:	9798288852756

Nonparametric Estimation of Latent Treatment Effects for Two Armed Clinical Trials Using Infinite Mixture Models /
Ellis, Benjamin Jefferson,

Nonparametric Estimation of Latent Treatment Effects for Two Armed Clinical Trials Using Infinite Mixture Models /Benjamin Jefferson Ellis. - 1 electronic resource (212 pages)

Source: Dissertations Abstracts International, Volume: 87-01, Section: B.

Control versus treatment clinical trials are prone to averaging over individual effects to produce a one size fits all conclusion of whether a drug/procedure have efficacy, on average. However, it is often the case that subjects have their own unique response to treatment that are not necessarily captured by an average effect size. We have developed an infinite mixture model that can produce a nonparametric estimate of the latent distribution of treatment effects for a population. The estimated distribution of treatment effects enables researchers to make conclusions about what percentage of the population will have a treatment effect of a specified size (i.e. medically significant), without assuming a parametric form on their data. This development opens the door for personalized medicine to be statistically modeled in two arm clinical trials.

English

ISBN: 9798288852756Subjects--Topical Terms:

182057
Statistics.
Subjects--Index Terms:

Convolutions

Nonparametric Estimation of Latent Treatment Effects for Two Armed Clinical Trials Using Infinite Mixture Models /
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500 $a Advisors: Jeske, Daniel R. Committee members: Kurum, Esra; Yao, Weixin.
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520 $a Control versus treatment clinical trials are prone to averaging over individual effects to produce a one size fits all conclusion of whether a drug/procedure have efficacy, on average. However, it is often the case that subjects have their own unique response to treatment that are not necessarily captured by an average effect size. We have developed an infinite mixture model that can produce a nonparametric estimate of the latent distribution of treatment effects for a population. The estimated distribution of treatment effects enables researchers to make conclusions about what percentage of the population will have a treatment effect of a specified size (i.e. medically significant), without assuming a parametric form on their data. This development opens the door for personalized medicine to be statistically modeled in two arm clinical trials.
546 $a English
590 $a School code: 0032
650 4 $a Statistics. $3 182057
650 4 $a Medicine. $3 193819
650 4 $a Biostatistics. $3 339693
650 4 $a Bioinformatics. $3 194415
653 $a Convolutions
653 $a Individual
653 $a Personalized medicine
653 $a Infinite mixture model
653 $a Personal
653 $a Shift
690 $a 0463
690 $a 0564
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710 2 $a University of California, Riverside. $b Applied Statistics. $e degree granting institution. $3 1010352
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