ArticleStatistical methods in medical research2020
Optimal designs for group randomized trials and group administered treatments with outcomes at the subject and group level.
Article in Statistical methods in medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Optimal allocation to treatments in a sequential multiple assignment randomized trial.Statistical methods in medical research · 2021Trial
- Optimal group sizes for testing group mean differences using the Bayes factor.Journal of applied statistics · 2026Article
- More Power for Less Money: Statistical, Power, and Cost Analyses That Account for Intracluster Correlation in Experiments with Same-Group Cage Mates.Journal of the American Association for Laboratory Animal Science : JAALAS · 2025Article
- Optimal study designs for cluster randomised trials: An overview of methods and results.Statistical methods in medical research · 2023Review
- Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity.Statistics in medicine · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
With group randomized trials complete groups of subject are randomized to treatment conditions. Such grouping also occurs in individually randomized trials where treatment is administered in groups. Outcomes may be measured at the level of the subject, but also at the level of the group. The optimal design determines the number of groups and the number of subjects per group in the intervention and control conditions. It is found by taking a budgetary constraint into account, where costs are associated with implementing the intervention and control, and with taking measurements on subject and groups. The optimal design is found such that the effect of treatment is estimated with highest efficiency, and the total costs do not exceed the budget that is available. The design that is optimal for the outcome at the subject level is not necessarily optimal for the outcome at the group level. Multiple-objective optimal designs consider both outcomes simultaneously. Their aim is to find a design that has high efficiencies for both outcome measures. An Internet application for finding the multiple-objective optimal design is demonstrated on the basis of an example from smoking prevention in primary education, and another example on consultation time in primary care.
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