This text provides the most comprehensive treatment of the design and analytic issues involved in group-randomized trials. GRTs are comparative studies conducted to evaluate the effect of a health promotion intervention in which the units of assignment are identifiable groups (e.g., schools, worksites) and the units of observation are members of those groups (e.g., students, workers). The book reviews the underlying issues, the most widely used research designs, and analytic strategies. There is an emphasis on mixed-model regression, with two chapters illustrating the analytic methods in SAS PROC MIXED and GLIMMIX. There is also a detailed chapter on power analysis and sample size calculation.
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"This book is an important addition to any public health or medical library. It is well-written and much needed."--Doody's Journal
About the Author
Dr. Murray is the Chair of the Division of Epidemiology in the School of Public Health at The Ohio State University. Since 1978, he has evaluated intervention programs designed to improve the public health. Since 1989, he has focused on the design and analysis of group-randomized trials. He collaborates with colleagues around the country on the design, implementation and evaluation of group-randomized trials. He also conducts research to develop and test methods for the analysis of data from group-randomized trials. Dr. Murray teaches courses on the design and analysis of group-randomized trials and was the first Chair of the Community Level Health Promotion study section at NIH, which reviews a number of proposals for new group-randomized trials.
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4 of 4 people found the following review helpful:
4.0 out of 5 stars
Nice book with a major flaw,
By JerryWithaJ (USA) - See all my reviews
This review is from: Design and Analysis of Group- Randomized Trials (Hardcover)
Group-randomized trials are trials in which randomization takes place at a level higher than the individual subject. For example, for logistical reasons a comparison of two programs might be undertaken by randomizing communities to program, in which case the community rather than the resident is the unit of analysis.Even though Murray's text was published in 1998, it is still the outstanding book in the field. (Are there any others?) You will find reading this book to be a profitable experience if only because you can point to it (literally!) when telling someone that the number of observations is the number of CITIES that were randomized to be targeted with an anti-smoking campaign or not and NOT the number of RESIDENTS. The writing is clear and straightforward. To get the most out of it you should be comfortable with with mathematical models (use the Search Inside facility to look at the bottom of page 140, for example), which are used to describe things unambiguously rather than to develop mathemeatical theory. Even if you are not comfortable with mathematical models, there is a lot to be gained from Murray's discussion of the issues. The flaw is MAJOR. The book contains numerous analyses of data from the Minnesota Hearth Health Program. However, Murray is not able to provide the original data. With the original data, one could be an active learner by replicationg his analyses and trying other aproaches to see what effects they might have. Without the data, learning is much more passive. I struggled hard choosing the number of stars. I thought about 3 starts because, when you get right down to it, the analysis of group-randomized trials livolves nothing more than a particular kind of hierarchical model, which other texts discuss splendidly. However, if someone wanted to know about group randomized trials specifically, this is where I would send him/her, hence the 4 stars. If the data were available, the rating would be 5 stars without question.
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Inside This Book
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First Sentence:
Group-randomized trials are comparative studies in which (1) the units of assignment are identifiable groups and (2) the units of observation are members of those groups. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
nested cohort design, priori stratification, adjusted net difference, condition mean slopes, nobound option, post hoc stratification, general linear mixed model, cohort version, groups per condition, lsmeans effect, measurable heterogeneity, unadjusted analysis, common secular trend, analysis for diastolic blood pressure, difference between the two strata, positive intraclass, regression adjustment, observed residual variance, post hoc stratified analysis, multiple random effects, possible intervention effects, stratum survey, proc mixed statement, unstratified analysis, three random effects Key Phrases - Capitalized Phrases (CAPs): (learn more)
Extended Design, Generalized Linear Mixed Model, Coefficients Analysis of Diastolic Blood Pressure, Generalized Linear Model, Cov Parm Estimate, Log Likelihood, Condition Analysis of Diastolic Blood Pressure, Minnesota Heart Health Program, Akaike's Information Criterion, Central Limit Theorem, Condition Analysis of Smoking Status, Max Obs Per Subject, Schwarz's Bayesian Criterion, Months Source, University of Minnesota, Evaluation Program, Institute Inc, Priori Stratification With Stratum, Max Cols, Months Parameter Estimate, Priori Matching Source, Coding Time, Fixed Effect Source, Matrix Year, Schwarz's Bayesian Information Criterion New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Group-randomized trials are comparative studies in which (1) the units of assignment are identifiable groups and (2) the units of observation are members of those groups. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
nested cohort design, priori stratification, adjusted net difference, condition mean slopes, nobound option, post hoc stratification, general linear mixed model, cohort version, groups per condition, lsmeans effect, measurable heterogeneity, unadjusted analysis, common secular trend, analysis for diastolic blood pressure, difference between the two strata, positive intraclass, regression adjustment, observed residual variance, post hoc stratified analysis, multiple random effects, possible intervention effects, stratum survey, proc mixed statement, unstratified analysis, three random effects Key Phrases - Capitalized Phrases (CAPs): (learn more)
Extended Design, Generalized Linear Mixed Model, Coefficients Analysis of Diastolic Blood Pressure, Generalized Linear Model, Cov Parm Estimate, Log Likelihood, Condition Analysis of Diastolic Blood Pressure, Minnesota Heart Health Program, Akaike's Information Criterion, Central Limit Theorem, Condition Analysis of Smoking Status, Max Obs Per Subject, Schwarz's Bayesian Criterion, Months Source, University of Minnesota, Evaluation Program, Institute Inc, Priori Stratification With Stratum, Max Cols, Months Parameter Estimate, Priori Matching Source, Coding Time, Fixed Effect Source, Matrix Year, Schwarz's Bayesian Information Criterion New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
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book:
- SAS/STAT Software: Changes and Enhancements, Release 8.2 by SAS Publishing in Back Matter
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