Literature DB >> 11480307

An evaluation of analysis options for the one-group-per-condition design. Can any of the alternatives overcome the problems inherent in this design?

S P Varnell1, D M Murray, W L Baker.   

Abstract

This article addresses the analytic problems associated with a design in which one identifiable group is allocated to each treatment condition and members of those groups are measured to assess the intervention. Such designs are often called quasi-experiments if the groups are not randomized to conditions and group-randomized trials if the groups are randomized. They present special problems, and previous reports have argued against their use in efficacy or effectiveness trials. Even so, this design still appears with surprising frequency. This article presents the results from a new simulation study that underscores the analytic problems associated with this design.

Mesh:

Year:  2001        PMID: 11480307     DOI: 10.1177/0193841X0102500402

Source DB:  PubMed          Journal:  Eval Rev        ISSN: 0193-841X


  9 in total

Review 1.  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

Review 2.  Designing studies that would address the multilayered nature of health care.

Authors:  David M Murray; Michael Pennell; Dale Rhoda; Erinn M Hade; Electra D Paskett
Journal:  J Natl Cancer Inst Monogr       Date:  2010

Review 3.  Multilevel factorial experiments for developing behavioral interventions: power, sample size, and resource considerations.

Authors:  John J Dziak; Inbal Nahum-Shani; Linda M Collins
Journal:  Psychol Methods       Date:  2012-02-06

Review 4.  Individually randomized group treatment trials: a critical appraisal of frequently used design and analytic approaches.

Authors:  Sherri L Pals; David M Murray; Catherine M Alfano; William R Shadish; Peter J Hannan; William L Baker
Journal:  Am J Public Health       Date:  2008-06-12       Impact factor: 9.308

Review 5.  Best (but oft-forgotten) practices: designing, analyzing, and reporting cluster randomized controlled trials.

Authors:  Andrew W Brown; Peng Li; Michelle M Bohan Brown; Kathryn A Kaiser; Scott W Keith; J Michael Oakes; David B Allison
Journal:  Am J Clin Nutr       Date:  2015-05-27       Impact factor: 7.045

6.  Efficient Bayesian joint models for group randomized trials with multiple observation times and multiple outcomes.

Authors:  Xinyi Xu; Michael L Pennell; Bo Lu; David M Murray
Journal:  Stat Med       Date:  2012-06-25       Impact factor: 2.373

7.  Estimating the Effect of a Community-Based Intervention with Two Communities.

Authors:  Mark J van der Laan; Maya Petersen; Wenjing Zheng
Journal:  J Causal Inference       Date:  2013-05

8.  The effect of engaging unpaid informal providers on case detection and treatment initiation rates for TB and HIV in rural Malawi (Triage Plus): A cluster randomised health system intervention trial.

Authors:  George Bello; Brian Faragher; Lifah Sanudi; Ireen Namakhoma; Hastings Banda; Rasmus Malmborg; Rachael Thomson; S Bertel Squire
Journal:  PLoS One       Date:  2017-09-06       Impact factor: 3.240

Review 9.  Reporting of key methodological and ethical aspects of cluster trials in hemodialysis require improvement: a systematic review.

Authors:  Ahmed A Al-Jaishi; Kelly Carroll; Cory E Goldstein; Stephanie N Dixon; Amit X Garg; Stuart G Nicholls; Jeremy M Grimshaw; Charles Weijer; Jamie Brehaut; Lehana Thabane; P J Devereaux; Monica Taljaard
Journal:  Trials       Date:  2020-08-28       Impact factor: 2.279

  9 in total

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