國立虎尾科技大學 |

Nonparametric Methods for Analysis and Sizing of Cluster Randomization Trials With Baseline Measurements.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Nonparametric Methods for Analysis and Sizing of Cluster Randomization Trials With Baseline Measurements./
Author:	Yu, Chengchun.
Description:	1 online resource (163 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798380864596

Nonparametric Methods for Analysis and Sizing of Cluster Randomization Trials With Baseline Measurements.
Yu, Chengchun.

Nonparametric Methods for Analysis and Sizing of Cluster Randomization Trials With Baseline Measurements. - 1 online resource (163 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--The University of Western Ontario (Canada), 2023.

Includes bibliographical references

Cluster randomization trials are popular in situations where the intervention needs to be implemented at the cluster level, or logistical and/or financial reasons require the choice of randomization at the cluster level, or minimization of contamination is needed. It is very common for cluster trials to take measurements before randomization and again at follow-up, resulting in a clustered pretest-posttest design. For continuous outcomes, the cluster-adjusted analysis of covariance approach can be used to adjust for accidental bias and improve efficiency. However, a direct application of this method is inappropriate if the measurements are not on an interval scale, yet such data are very common in practice. In this thesis, we propose nonparametric methods for trials with a clustered pretest-posttest design, focusing on estimation of the treatment effect. We quantify treatment effects using the win probability, defined as the probability that a randomly selected subject in the treatment group has a more favourable outcome than one in the control group. The methods for data analysis and sample size planning for estimating win probability rely on subject-specific win fractions created from outcome measurements at baseline and follow-up. Specifically, the win fraction for a subject is given by the difference between the rank of the observation among all observations in the combined sample of two groups and its rank among observations in its own group divided by the sample size of the comparison group. The cluster-adjusted analysis of covariance is then applied to win fractions created from baseline and follow-up measurements. The proposed methods, which may be considered as an extension of Zou (2021) for follow-up measurements, are applicable to studies with binary, ordinal, count, and continuous outcomes without making parametric assumptions.Simulation results demonstrated that the methods for constructing confidence intervals for the win probability performed well in terms of coverage and average interval width, even when the number of clusters is small as 5 clusters per arm. The methods for sample size estimation also performed well in terms of the probability of achieving a pre-specified precision. The methods are illustrated using data from two published cluster randomization trials with SAS code provided.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380864596Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Cluster randomization trialsIndex Terms--Genre/Form:

554714
Electronic books.

Nonparametric Methods for Analysis and Sizing of Cluster Randomization Trials With Baseline Measurements.
LDR:03883ntm a22004097 4500 001 1145358
005 20240618081827.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798380864596
035 $a (MiAaPQ)AAI30879774
035 $a (MiAaPQ)oaiirlibuwocaetd12470
035 $a AAI30879774
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Yu, Chengchun. $3 1470658
245 1 0 $a Nonparametric Methods for Analysis and Sizing of Cluster Randomization Trials With Baseline Measurements.
264 0 $c 2023
300 $a 1 online resource (163 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Choi, Yun-hee;Zou, Guangyong.
502 $a Thesis (Ph.D.)--The University of Western Ontario (Canada), 2023.
504 $a Includes bibliographical references
520 $a Cluster randomization trials are popular in situations where the intervention needs to be implemented at the cluster level, or logistical and/or financial reasons require the choice of randomization at the cluster level, or minimization of contamination is needed. It is very common for cluster trials to take measurements before randomization and again at follow-up, resulting in a clustered pretest-posttest design. For continuous outcomes, the cluster-adjusted analysis of covariance approach can be used to adjust for accidental bias and improve efficiency. However, a direct application of this method is inappropriate if the measurements are not on an interval scale, yet such data are very common in practice. In this thesis, we propose nonparametric methods for trials with a clustered pretest-posttest design, focusing on estimation of the treatment effect. We quantify treatment effects using the win probability, defined as the probability that a randomly selected subject in the treatment group has a more favourable outcome than one in the control group. The methods for data analysis and sample size planning for estimating win probability rely on subject-specific win fractions created from outcome measurements at baseline and follow-up. Specifically, the win fraction for a subject is given by the difference between the rank of the observation among all observations in the combined sample of two groups and its rank among observations in its own group divided by the sample size of the comparison group. The cluster-adjusted analysis of covariance is then applied to win fractions created from baseline and follow-up measurements. The proposed methods, which may be considered as an extension of Zou (2021) for follow-up measurements, are applicable to studies with binary, ordinal, count, and continuous outcomes without making parametric assumptions.Simulation results demonstrated that the methods for constructing confidence intervals for the win probability performed well in terms of coverage and average interval width, even when the number of clusters is small as 5 clusters per arm. The methods for sample size estimation also performed well in terms of the probability of achieving a pre-specified precision. The methods are illustrated using data from two published cluster randomization trials with SAS code provided.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Mathematics. $3 527692
650 4 $a Applied mathematics. $3 1069907
653 $a Cluster randomization trials
653 $a Operating characteristic curve
653 $a Wilcoxon-Mann-Whitney statistic
653 $a Interval estimation
653 $a Cluster randomization
655 7 $a Electronic books. $2 local $3 554714
690 $a 0405
690 $a 0364
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Western Ontario (Canada). $3 1184598
773 0 $t Dissertations Abstracts International $g 85-06B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30879774 $z click for full text (PQDT)