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

TL;DR

This paper evaluates methods for accurately estimating site-specific effects and their distributions in multisite trials, emphasizing the importance of data availability and shrinkage strategies for optimal inference.

Contribution

It compares semiparametric prior modeling and tailored posterior summaries, providing guidance on their effectiveness based on data conditions.

Findings

Performance varies with data quantity and quality.

Shrinkage methods improve estimates in limited data scenarios.

Guidelines for choosing estimation strategies are proposed.

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.