Improving the Estimation of Site-Specific Effects and Their Distribution in Multisite Trials

In multisite trials, researchers are often interested in several inferential goals: estimating treatment effects for each site, ranking these effects, and studying their distribution. This study seeks to identify optimal methods for estimating these targets. Through a comprehensive simulation study, we assess two strategies and their combined effects: semiparametric modeling of the prior distribution and alternative posterior summary methods tailored to minimize specific loss functions. Our findings highlight that the success of different estimation strategies depends largely on the amount of within-site and between-site information available from the data. We discuss how our results can guide balancing the trade-offs associated with shrinkage in limited data environments.

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Publication Details

Journal
Journal of Educational and Behavioral Statistics
Published
2024-08-30
DOI
https://doi.org/10.3102/10769986241254286
Citations
2
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
1.42
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article

Improving the Estimation of Site-Specific Effects and Their Distribution in Multisite Trials

Luke Miratrix, Avi Feller, Jonathan Che, Sophia Rabe‐Hesketh et al.
2 citations
Journal of Educational and Behavioral Statistics
Statistical Methods in Clinical Trials
1.42
article

Improving the Estimation of Site-Specific Effects and Their Distribution in Multisite Trials

Luke Miratrix, Avi Feller, Jonathan Che, Sophia Rabe‐Hesketh, Joon-Ho Lee
article en
2 citations

Abstract

In multisite trials, researchers are often interested in several inferential goals: estimating treatment effects for each site, ranking these effects, and studying their distribution. This study seeks to identify optimal methods for estimating these targets. Through a comprehensive simulation study, we assess two strategies and their combined effects: semiparametric modeling of the prior distribution and alternative posterior summary methods tailored to minimize specific loss functions. Our findings highlight that the success of different estimation strategies depends largely on the amount of within-site and between-site information available from the data. We discuss how our results can guide balancing the trade-offs associated with shrinkage in limited data environments.

Journal of Educational and Behavioral StatisticsVol. 50(5)
Exponent (United States) (US), University of Alabama (US), Harvard University Press (US), University of California, Berkeley (US)
Partnerships for the goals
Openalex Percentile: Top 18%
Statistical Methods in Clinical Trials
1.42
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