Empirical-Bayes adjustments for multiple comparisons are sometimes useful.
Rothman (Epidemiology 1990;1:43-46) recommends against adjustments for multiple comparisons. Implicit in his recommendation, however, is an assumption that the sole objective of the data analysis is to report and scientifically interpret the data. We concur with his recommendation when this assumption is correct and one is willing to abandon frequentist interpretations of the summary statistics. Nevertheless, there are situations in which an additional or even primary goal of analysis is to reach a set of decisions based on the data. In such situations, Bayes and empirical-Bayes adjustments can provide a better basis for the decisions than conventional procedures.
Greenland
S
S
Department of Epidemiology, UCLA School of Public Health 90024-1772.
Robins
J M
JM
D001499:Bayes Theorem; D004544:El Salvador / Q000453:epidemiology; D004812:Epidemiologic Methods; D006801:Humans; D015233:Models, Statistical; D015995:Prevalence; D012306:Risk; D014123:Toxoplasmosis / Q000453:epidemiology