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Combining information for heterogene...

Liu, Dungang.

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Combining information for heterogeneous studies and rare events studies: A confidence distribution approach.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Combining information for heterogeneous studies and rare events studies: A confidence distribution approach./
Author:	Liu, Dungang.
Description:	94 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-02(E), Section: B.
Contained By:	Dissertation Abstracts International74-02B(E).
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3541185
ISBN:	9781267670991

Combining information for heterogeneous studies and rare events studies: A confidence distribution approach.
Liu, Dungang.

Combining information for heterogeneous studies and rare events studies: A confidence distribution approach. - 94 p.

Source: Dissertation Abstracts International, Volume: 74-02(E), Section: B.

Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2012.

This dissertation develops efficient statistical methodologies for combining information from independent sources. The developments focus on two settings where the studies are heterogeneous or the studies involve rare events. In these settings, the conventional combining approaches often lead to inefficient or even invalid statistical inference. In this dissertation, we propose effective and efficient combining approaches using confidence distributions. The proposed approaches are justified both theoretically and numerically. They are also shown to be superior to the conventional approaches.

ISBN: 9781267670991Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Combining information for heterogeneous studies and rare events studies: A confidence distribution approach.
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100 1 $a Liu, Dungang. $3 2093233
245 1 0 $a Combining information for heterogeneous studies and rare events studies: A confidence distribution approach.
300 $a 94 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-02(E), Section: B.
500 $a Adviser: Regina Liu.
502 $a Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2012.
520 $a This dissertation develops efficient statistical methodologies for combining information from independent sources. The developments focus on two settings where the studies are heterogeneous or the studies involve rare events. In these settings, the conventional combining approaches often lead to inefficient or even invalid statistical inference. In this dissertation, we propose effective and efficient combining approaches using confidence distributions. The proposed approaches are justified both theoretically and numerically. They are also shown to be superior to the conventional approaches.
520 $a Combining information from multiple studies, often referred to as meta-analysis in the literature, has been used extensively in many fields, including health sciences, social sciences, and others. However, there remain many unresolved problems on how to effectively and efficiently combine information. For example,
520 $a • Heterogeneous studies -- When the effect of interest is not estimable in heterogeneous studies (e.g., indirect evidence), how can we utilize these studies to perform meta-analysis?
520 $a • Rare events studies -- For clinical trials with rare events, how can we perform meta-analysis and incorporate studies with zero events in the analysis without using artificial continuity corrections or relying on large sample theory?
520 $a To address these challenging but recurrent problems, this dissertation develops new meta-analysis approaches based on combining confidence distributions. Roughly speaking, a confidence distribution refers to a sample-dependent distribution function on the parameter space with desirable inferential properties in terms of repeated sampling performance. It can be viewed as a frequentist counterpart to the posterior distribution in Bayesian inference. In this dissertation, we show the combination of confidence distributions has desirable properties which are lacking in the conventional approaches. Specifically, 1) in the presence of heterogeneous studies, the proposed approach integrates direct and indirect evidence and achieves asymptotic efficiency; 2) for rare event studies, the proposed approach yields exact inference and incorporates all the studies in the analysis without using artificial continuity corrections for zero events. These properties are demonstrated numerically in simulation studies and real data examples, including flight landing safety data collected by the Federal Aviation Administration and drug safety data collected in diabetes clinical trials.
590 $a School code: 0190.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
690 $a 0308
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710 2 $a Rutgers The State University of New Jersey - New Brunswick. $b Graduate School - New Brunswick. $3 1019196
773 0 $t Dissertation Abstracts International $g 74-02B(E).
790 $a 0190
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3541185