Empirical Bayes adjustments for multiple results in hypothesis-generating or surveillance studies

Traditional methods of adjustment for multiple comparisons (e.g., Bonferron i adjustments) have fallen into disuse In epidemiological studies. However, alternative kinds of adjustment for data with multiple comparisons may som etimes be advisable, When a large number of comparisons are made, and when there is a high cost to investigating false positive leads, empirical or se mi-Raves adjustments may help in the selection of the most promising leads. Here we offer an example of such adjustments in a large surveillance data set of occupation and cancer in Nordic countries, in which we used empirica l Bayes (EB) adjustments to evaluate standardized incidence ratios (SIRs) f or cancer and occupation among craftsmen and laborers. For men, there were 642 SIRs, of which 138 (21%) had a P < 0.05 (13% positive with SIR > 1.0 an d 8% negative with SIR less than or equal to 1.0) when testing the null hyp othesis of no cancer/occupation association; some of these were probably du e to confounding by nonoccupational risk factors (e.g., smoking). After EB adjustments, there were 95 (15%) SIRs with P < 0.05 (10% positive and 5% ne gative). For women, there were 373 SIRs, of which 37 (10%) had P < 0.05 bef ore adjustment (6% positive and 4% negative) and 13 (3%) had P < 0.05 after adjustment (2% positive and 1% negative). Several known associations were confirmed after EB adjustment (e.g., pleural cancer among plumbers, origina l SIR 3.2 (95% confidence interval, 2.5-4.1), adjusted SIR 2.0 (95% confide nce Interval, 1.6-2.4), EB can produce more accurate estimates of relative risk by shrinking imprecise outliers toward the mean, which may reduce the number of false positives otherwise flagged for further investigation. For example, liver cancer among chimney sweepers was reduced from an original S IR of 2.2 (range, 1.1-4.4) to an adjusted SIR of 1.1 (range, 0.9-1.4), A po tentially important future application for EB is studies of gene-environmen t-disease interactions, in which hundreds of polymorphisms may be evaluated with dozens of environmental risk factors in large cohort studies, produci ng thousands of associations.