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Hypothesis testing in GWAS and stati...

Swanson, David Michael.

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Hypothesis testing in GWAS and statistical issues with compensation in clinical trials.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Hypothesis testing in GWAS and statistical issues with compensation in clinical trials./
作者:	Swanson, David Michael.
面頁冊數:	60 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
Contained By:	Dissertation Abstracts International74-10B(E).
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3567085
ISBN:	9781303187216

Hypothesis testing in GWAS and statistical issues with compensation in clinical trials.
Swanson, David Michael.

Hypothesis testing in GWAS and statistical issues with compensation in clinical trials. - 60 p.

Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.

Thesis (Ph.D.)--Harvard University, 2013.

We first show theoretically and in simulation how power varies as a function of SNP correlation structure with currently-implemented gene-based testing methods. We propose alternative testing methods whose power does not vary with the correlation structure. We then propose hypothesis tests for detecting prevalence-incidence bias in case-control studies, a bias perhaps overrepresented in GWAS due to currently used study designs. Lastly, we hypothesize how different incentive structures used to keep clinical trial participants in studies may interact with a background of dependent censoring and result in variation in the bias of the Kaplan-Meier survival curve estimator.

ISBN: 9781303187216Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Hypothesis testing in GWAS and statistical issues with compensation in clinical trials.
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500 $a Advisers: Rebecca Betensky; Christoph Lange.
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520 $a We first show theoretically and in simulation how power varies as a function of SNP correlation structure with currently-implemented gene-based testing methods. We propose alternative testing methods whose power does not vary with the correlation structure. We then propose hypothesis tests for detecting prevalence-incidence bias in case-control studies, a bias perhaps overrepresented in GWAS due to currently used study designs. Lastly, we hypothesize how different incentive structures used to keep clinical trial participants in studies may interact with a background of dependent censoring and result in variation in the bias of the Kaplan-Meier survival curve estimator.
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