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Statistical methods for effect estim...

Cefalu, Matthew Steven.

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Statistical methods for effect estimation in biomedical research: Robustness and efficiency.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical methods for effect estimation in biomedical research: Robustness and efficiency./
Author:	Cefalu, Matthew Steven.
Description:	113 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
Contained By:	Dissertation Abstracts International74-10B(E).
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3566831
ISBN:	9781303183751

Statistical methods for effect estimation in biomedical research: Robustness and efficiency.
Cefalu, Matthew Steven.

Statistical methods for effect estimation in biomedical research: Robustness and efficiency. - 113 p.

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

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

Practical application of statistics in biomedical research is predicated on the notion that one can readily return valid effect estimates of the health consequences of treatments (exposures) that are being studied. The goal as statisticians should be to provide results that are scientifically useful, to use the available data as efficiently as possible, to avoid unnecessary assumptions, and, if necessary, develop methods that are robust to incorrect assumptions. In this dissertation, I provide methods for effect estimation that meet these goals. I consider three scenarios: (1) clustered binary outcomes; (2) continuous outcomes with a binary treatment; and (3) continuous outcomes with potentially missing continuous exposure. In each of these settings, I discuss the shortfalls of current statistical methods for effect estimation available in the literature and propose new and innovative methods that meet the previously stated goals. The validity of each proposed estimator is theoretically verified using asymptotic arguments, and the finite sample behavior is studied through simulation.

ISBN: 9781303183751Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Statistical methods for effect estimation in biomedical research: Robustness and efficiency.
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100 1 $a Cefalu, Matthew Steven. $3 2104957
245 1 0 $a Statistical methods for effect estimation in biomedical research: Robustness and efficiency.
300 $a 113 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
500 $a Adviser: Francesca Dominici.
502 $a Thesis (Ph.D.)--Harvard University, 2013.
520 $a Practical application of statistics in biomedical research is predicated on the notion that one can readily return valid effect estimates of the health consequences of treatments (exposures) that are being studied. The goal as statisticians should be to provide results that are scientifically useful, to use the available data as efficiently as possible, to avoid unnecessary assumptions, and, if necessary, develop methods that are robust to incorrect assumptions. In this dissertation, I provide methods for effect estimation that meet these goals. I consider three scenarios: (1) clustered binary outcomes; (2) continuous outcomes with a binary treatment; and (3) continuous outcomes with potentially missing continuous exposure. In each of these settings, I discuss the shortfalls of current statistical methods for effect estimation available in the literature and propose new and innovative methods that meet the previously stated goals. The validity of each proposed estimator is theoretically verified using asymptotic arguments, and the finite sample behavior is studied through simulation.
590 $a School code: 0084.
650 4 $a Biology, Biostatistics. $3 1018416
690 $a 0308
710 2 $a Harvard University. $b Biostatistics. $3 2104931
773 0 $t Dissertation Abstracts International $g 74-10B(E).
790 $a 0084
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3566831