Quantifying Power and Bias in Cluster Randomized Trials Using Mixed Models vs. Cluster-Level Analysis in the Presence of Missing Data: A Simulation Study

Persistent Link:
http://hdl.handle.net/10150/613376
Title:
Quantifying Power and Bias in Cluster Randomized Trials Using Mixed Models vs. Cluster-Level Analysis in the Presence of Missing Data: A Simulation Study
Author:
Vincent, Brenda
Issue Date:
2016
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
In cluster randomized trials (CRTs), groups are randomized to treatment arms rather than individuals while the outcome is assessed on the individuals within each cluster. Individuals within clusters tend to be more similar than in a randomly selected sample, which poses issues with dependence, which may lead to underestimated standard errors if ignored. To adjust for the correlation between individuals within clusters, two main approaches are used to analyze CRTs: cluster-level and individual-level analysis. In a cluster-level analysis summary measures are obtained for each cluster and then the two sets of cluster-specific measures are compared, such as with a t-test of the cluster means. A mixed model which takes into account cluster membership is an example of an individual-level analysis. We used a simulation study to quantify and compare power and bias of these two methods. We further take into account the effect of missing data. Complete datasets were generated and then data were deleted to simulate missing completely at random (MCAR) and missing at random (MAR) data. A balanced design, with two treatment groups and two time points was assumed. Cluster size, variance components (including within-subject, within-cluster and between-cluster variance) and proportion of missingness were varied to simulate common scenarios seen in practice. For each combination of parameters, 1,000 datasets were generated and analyzed. Results of our simulation study indicate that cluster-level analysis resulted in substantial loss of power when data were MAR. Individual-level analysis had higher power and remained unbiased, even with a small number of clusters.
Type:
text; Electronic Thesis
Keywords:
cluster randomized trial; missing data; mixed model; power; Biostatistics; bias
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Biostatistics
Degree Grantor:
University of Arizona
Advisor:
Bell, Melanie

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleQuantifying Power and Bias in Cluster Randomized Trials Using Mixed Models vs. Cluster-Level Analysis in the Presence of Missing Data: A Simulation Studyen_US
dc.creatorVincent, Brendaen
dc.contributor.authorVincent, Brendaen
dc.date.issued2016-
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.abstractIn cluster randomized trials (CRTs), groups are randomized to treatment arms rather than individuals while the outcome is assessed on the individuals within each cluster. Individuals within clusters tend to be more similar than in a randomly selected sample, which poses issues with dependence, which may lead to underestimated standard errors if ignored. To adjust for the correlation between individuals within clusters, two main approaches are used to analyze CRTs: cluster-level and individual-level analysis. In a cluster-level analysis summary measures are obtained for each cluster and then the two sets of cluster-specific measures are compared, such as with a t-test of the cluster means. A mixed model which takes into account cluster membership is an example of an individual-level analysis. We used a simulation study to quantify and compare power and bias of these two methods. We further take into account the effect of missing data. Complete datasets were generated and then data were deleted to simulate missing completely at random (MCAR) and missing at random (MAR) data. A balanced design, with two treatment groups and two time points was assumed. Cluster size, variance components (including within-subject, within-cluster and between-cluster variance) and proportion of missingness were varied to simulate common scenarios seen in practice. For each combination of parameters, 1,000 datasets were generated and analyzed. Results of our simulation study indicate that cluster-level analysis resulted in substantial loss of power when data were MAR. Individual-level analysis had higher power and remained unbiased, even with a small number of clusters.en
dc.typetexten
dc.typeElectronic Thesisen
dc.subjectcluster randomized trialen
dc.subjectmissing dataen
dc.subjectmixed modelen
dc.subjectpoweren
dc.subjectBiostatisticsen
dc.subjectbiasen
thesis.degree.nameM.S.en
thesis.degree.levelmastersen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineBiostatisticsen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorBell, Melanieen
dc.contributor.committeememberRoe, Deniseen
dc.contributor.committeememberHsu, Chiu-Hsiehen
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