Literature DB >> 11274517

Selected statistical issues in group randomized trials.

Z Feng1, P Diehr, A Peterson, D McLerran.   

Abstract

Group randomized trials (GRTs) in public health research typically use a small number of randomized groups with a relatively large number of participants per group. Two fundamental features characterize GRTs: a positive correlation of outcomes within a group, and the small number of groups. Appropriate consideration of these fundamental features is essential for design and analysis. This paper presents the fundamental features of GRTs and the importance of considering these features in design and analysis. It also reviews and contrasts the main analytic methods proposed for GRTs, emphasizing the assumptions required to make these methods valid and efficient. Also discussed are various design issues, along with guidelines for choosing among them. A real data example illustrates these issues and methods.

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Year:  2001        PMID: 11274517     DOI: 10.1146/annurev.publhealth.22.1.167

Source DB:  PubMed          Journal:  Annu Rev Public Health        ISSN: 0163-7525            Impact factor:   21.981


  38 in total

1.  Pitfalls of and controversies in cluster randomization trials.

Authors:  Allan Donner; Neil Klar
Journal:  Am J Public Health       Date:  2004-03       Impact factor: 9.308

Review 2.  Design and analysis of group-randomized trials: a review of recent methodological developments.

Authors:  David M Murray; Sherri P Varnell; Jonathan L Blitstein
Journal:  Am J Public Health       Date:  2004-03       Impact factor: 9.308

3.  A comparison of the statistical power of different methods for the analysis of repeated cross-sectional cluster randomization trials with binary outcomes.

Authors:  Peter C Austin
Journal:  Int J Biostat       Date:  2010-03-29       Impact factor: 0.968

4.  Finite-sample corrected generalized estimating equation of population average treatment effects in stepped wedge cluster randomized trials.

Authors:  JoAnna M Scott; Allan deCamp; Michal Juraska; Michael P Fay; Peter B Gilbert
Journal:  Stat Methods Med Res       Date:  2014-09-29       Impact factor: 3.021

5.  Alternatives to Logistic Regression Models when Analyzing Cluster Randomized Trials with Binary Outcomes.

Authors:  Francis L Huang
Journal:  Prev Sci       Date:  2021-04-06

6.  Acute alcohol use among suicide decedents in 14 US states: impacts of off-premise and on-premise alcohol outlet density.

Authors:  Norman Giesbrecht; Nathalie Huguet; Lauren Ogden; Mark S Kaplan; Bentson H McFarland; Raul Caetano; Kenneth R Conner; Kurt B Nolte
Journal:  Addiction       Date:  2014-11-13       Impact factor: 6.526

7.  Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes.

Authors:  Peng Li; David T Redden
Journal:  Stat Med       Date:  2014-10-24       Impact factor: 2.373

Review 8.  Cluster-Randomized Studies.

Authors:  Eva Lorenz; Sascha Köpke; Holger Pfaff; Maria Blettner
Journal:  Dtsch Arztebl Int       Date:  2018-03-09       Impact factor: 5.594

9.  The GoodNEWS (Genes, Nutrition, Exercise, Wellness, and Spiritual Growth) Trial: a community-based participatory research (CBPR) trial with African-American church congregations for reducing cardiovascular disease risk factors--recruitment, measurement, and randomization.

Authors:  Mark J DeHaven; Maria A Ramos-Roman; Nora Gimpel; JoAnn Carson; James DeLemos; Sue Pickens; Chris Simmons; Tiffany Powell-Wiley; Kamakki Banks-Richard; Kerem Shuval; Julie Duvahl; Julie Duval; Liyue Tong; Natalie Hsieh; Jenny J Lee
Journal:  Contemp Clin Trials       Date:  2011-06-02       Impact factor: 2.226

10.  Analysis of group randomized trials with multiple binary endpoints and small number of groups.

Authors:  Ji-Hyun Lee; Michael J Schell; Richard Roetzheim
Journal:  PLoS One       Date:  2009-10-21       Impact factor: 3.240

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