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Statistical methods for the assessme...

Sharkey, Brian Joseph.

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Statistical methods for the assessment of safety and efficacy in HIV clinical trials.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical methods for the assessment of safety and efficacy in HIV clinical trials./
Author:	Sharkey, Brian Joseph.
Description:	75 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=3567062
ISBN:	9781303186974

Statistical methods for the assessment of safety and efficacy in HIV clinical trials.
Sharkey, Brian Joseph.

Statistical methods for the assessment of safety and efficacy in HIV clinical trials. - 75 p.

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

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

Evaluation of different treatments for HIV should take into account the relative balance of safety and efficacy for each treatment. Often time in HIV clinical trials the primary efficacy outcome measure is time to virologic failure, analyzed in an intention-to-treat manner ignoring the changes from the randomized regimens which occur in a reasonable proportion of study participants, often due to treatment limiting adverse events. Clinically, there is therefore considerable interest in also comparing regimens with respect to the competing outcomes of virologic failure and treatment-limiting adverse events leading to discontinuation of the initial randomized regimen.

ISBN: 9781303186974Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Statistical methods for the assessment of safety and efficacy in HIV clinical trials.
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020 $a 9781303186974
035 $a (MiAaPQ)AAI3567062
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100 1 $a Sharkey, Brian Joseph. $3 2104959
245 1 0 $a Statistical methods for the assessment of safety and efficacy in HIV clinical trials.
300 $a 75 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
500 $a Adviser: Judith Lok.
502 $a Thesis (Ph.D.)--Harvard University, 2013.
520 $a Evaluation of different treatments for HIV should take into account the relative balance of safety and efficacy for each treatment. Often time in HIV clinical trials the primary efficacy outcome measure is time to virologic failure, analyzed in an intention-to-treat manner ignoring the changes from the randomized regimens which occur in a reasonable proportion of study participants, often due to treatment limiting adverse events. Clinically, there is therefore considerable interest in also comparing regimens with respect to the competing outcomes of virologic failure and treatment-limiting adverse events leading to discontinuation of the initial randomized regimen.
520 $a In Chapter 1, we propose an estimator of the cumulative incidence function in the presence of multiple types of censoring mechanisms. In a controlled clinical trial, it is quite reasonable to assume that censoring can occur for several reasons: some non-informative, others informative. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator of the cumulative incidence function. We apply our method to evaluate the safety and efficacy of two anti-HIV regimens in a study conducted by the AIDS Clinical Trial Group, ACTG A5095.
520 $a In Chapter 2, we provide a detailed example of the use of competing risks methods to an application in which there is similar interest in more than one of the competing risks. Specifically, we are interested in evaluating treatment failure in HIV, where the competing risks of failure are failure due to treatment-limiting adverse events or failure due to virologic failure because of lack or loss of suppression of viral load.
520 $a In Chapter 3, we develop a framework for analyzing competing risks when there is interest in more than one competing risk. This framework is developed in the context of a randomized clinical trial where the familywise error rate must be controlled. We tailor our approach to the HIV example in Chapter 2. We present several different methods for evaluating composite hypotheses and evaluate their performance under null and alternative hypotheses.
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=3567062