Which methodology improves the accuracy of safety effectiveness estimates by shrinking site estimates toward a global mean?

• A. Empirical Bayes method
• B. Simple average
• C. Ordinary least squares regression
• D. Randomization

Answer: (A)

Shrinking site estimates toward a global mean improves accuracy by balancing what each site observed with information from all sites. The Empirical Bayes method treats each site's true safety effectiveness as drawn from a common distribution and uses the data to estimate that distribution. Each site's estimate then becomes a weighted average of its own observed value and the overall mean, with weights dictated by how precise that site’s data are. When a site has a small sample and noisy data, the estimate is pulled more toward the global mean; when a site has strong, precise data, it stays closer to its own observation. This shrinkage reduces random fluctuations and lowers overall estimation error, especially across many sites with varying data quality.

Other approaches don’t provide this targeted shrinkage. A simple average sticks sites into a single mean without adjusting individual estimates toward it. Ordinary least squares regression focuses on modeling relationships rather than stabilizing site-level estimates toward a common value. Randomization relates to study design rather than how estimates are adjusted after data are collected.