東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Design and Analysis of Cluster Rando...

Balzer, Laura.

FindBook

Google Book

Amazon

博客來

Design and Analysis of Cluster Randomized Trials with Application to HIV Prevention and Treatment.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Design and Analysis of Cluster Randomized Trials with Application to HIV Prevention and Treatment./
作者:	Balzer, Laura.
面頁冊數:	131 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-01(E), Section: B.
Contained By:	Dissertation Abstracts International77-01B(E).
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3720361
ISBN:	9781339013381

Design and Analysis of Cluster Randomized Trials with Application to HIV Prevention and Treatment.
Balzer, Laura.

Design and Analysis of Cluster Randomized Trials with Application to HIV Prevention and Treatment. - 131 p.

Source: Dissertation Abstracts International, Volume: 77-01(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2015.

This dissertation is focused on the development of the optimal design and analysis for cluster randomized trials. Specifically, we tackle three common questions: whether or not to pair-match clusters, which causal parameter best captures the intervention effect, and how to select the adjustment set for the analysis. We begin by introducing a formal framework for causal inference in Chapter 1. Throughout, the Sustainable East Africa Research in Community Health (SEARCH) trial serves as the motivating example (NCT01864603). SEARCH is an ongoing community randomized trial to evaluate the impact of immediate and streamlined antiretroviral therapy on HIV incidence in rural East Africa.

ISBN: 9781339013381Subjects--Topical Terms:

1002712
Biostatistics.

Design and Analysis of Cluster Randomized Trials with Application to HIV Prevention and Treatment.
LDR:04785nmm a2200325 4500 001 2077560
005 20161114130327.5
008 170521s2015 ||||||||||||||||| ||eng d
020 $a 9781339013381
035 $a (MiAaPQ)AAI3720361
035 $a AAI3720361
040 $a MiAaPQ $c MiAaPQ
100 1 $a Balzer, Laura. $3 3193070
245 1 0 $a Design and Analysis of Cluster Randomized Trials with Application to HIV Prevention and Treatment.
300 $a 131 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-01(E), Section: B.
500 $a Advisers: Mark J. van der Laan; Maya L. Petersen.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2015.
520 $a This dissertation is focused on the development of the optimal design and analysis for cluster randomized trials. Specifically, we tackle three common questions: whether or not to pair-match clusters, which causal parameter best captures the intervention effect, and how to select the adjustment set for the analysis. We begin by introducing a formal framework for causal inference in Chapter 1. Throughout, the Sustainable East Africa Research in Community Health (SEARCH) trial serves as the motivating example (NCT01864603). SEARCH is an ongoing community randomized trial to evaluate the impact of immediate and streamlined antiretroviral therapy on HIV incidence in rural East Africa.
520 $a In Chapter 2, we consider pair-matching, an intuitive design strategy to protect study validity and to potentially increase power in randomized trials. In a common design, candidate units are identified, and their baseline characteristics are used to create the best n/2 matched pairs. Within the resulting pairs, the intervention is randomized, and the outcomes are measured at the end of follow-up. We consider this design to be adaptive, because the construction of the matched pairs depends on the baseline covariates of all candidate units. As a consequence, the observed data cannot be considered as n/2 independent, identically distributed (i.i.d.) pairs of units, as common practice assumes. Instead, the observed data consist of n dependent units. Chapter 2 explores the consequences of adaptive pair-matching in randomized trials for estimation of the conditional average treatment effect (CATE): the intervention effect, given the measured covariates of the n study units. We contrast the unadjusted estimator with TMLE and show substantial efficiency gains from matching and further gains with adjustment.
520 $a In Chapter 3, we compare three causal parameters: the population, conditional and sample average treatment effects. Using a structural causal model, we explicitly define each parameter, discuss interpretation, and formally examine identifiability. To the best of our knowledge, Chapter 3 is the first to propose using TMLE for estimation and inference of the sample effect. In most settings, the sample parameter will be estimated more efficiently than the conditional parameter, which will, in turn, be estimated more efficiently than the population parameter. Finite sample simulations illustrate the potential gains in precision and power from selecting the sample effect as the target of inference.
520 $a Finally in Chapter 4, we discuss adjustment for measured covariates during the analysis to reduce variance and increase power in randomized trials. To avoid misleading inference, the analysis plan must be pre-specified. However, it is often unclear a priori which baseline covariates (if any) should be included in the analysis. In the SEARCH trial, for example, there are 16 matched pairs of communities and many potential adjustment variables, including region, HIV prevalence, male circumcision coverage and measures of community-level viral load. In Chapter 4, we propose a rigorous procedure to data-adaptively select the adjustment set, which maximizes the efficiency of the analysis. Specifically, we use cross-validation to select from a pre-specified library the candidate TMLE that minimizes the estimated variance. For further gains in precision, we also propose a collaborative procedure for estimating the known exposure mechanism. Our small sample simulations demonstrate the promise of the methodology to maximize study power, while maintaining nominal confidence interval coverage. Our procedure is tailored to the scientific question (sample vs. population treatment effect) and study design (pair-matched or not) and alleviates many of the common concerns.
590 $a School code: 0028.
650 4 $a Biostatistics. $3 1002712
650 4 $a Public health. $3 534748
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0573
690 $a 0463
710 2 $a University of California, Berkeley. $b Biostatistics. $3 2101048
773 0 $t Dissertation Abstracts International $g 77-01B(E).
790 $a 0028
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3720361