This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Steenland, K. Articles by Boffetta, P. Search for Related Content PubMed PubMed Citation Articles by Steenland, K. Articles by Boffetta, P.

Empirical Bayes Adjustments for Multiple Results in Hypothesis-generating or Surveillance Studies¹

National Institute for Occupational Safety and Health, Cincinnati, Ohio 45226 [K. S.]; School of Math and Statistics, University of Plymouth, Drake Circus, Plymouth PL48AA, United Kingdom [I. B.]; Department of Epidemiology, University of California Los Angeles School of Public Health, Los Angeles, California 90095-1772 [S. G.]; and IARC, 69372 Lyon, France [P. B.]

Traditional methods of adjustment for multiple comparisons (e.g., Bonferroni adjustments) have fallen into disuse in epidemiological studies. However, alternative kinds of adjustment for data with multiple comparisons may sometimes be advisable. When a large number of comparisons are made, and when there is a high cost to investigating false positive leads, empirical or semi-Bayes 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 empirical Bayes (EB) adjustments to evaluate standardized incidence ratios (SIRs) for 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 and 8% negative with SIR 1.0) when testing the null hypothesis of no cancer/occupation association; some of these were probably due 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% negative). For women, there were 373 SIRs, of which 37 (10%) had P < 0.05 before 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, original SIR 3.2 (95% confidence interval, 2.5–4.1), adjusted SIR 2.0 (95% confidence 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 SIR of 2.2 (range, 1.1–4.4) to an adjusted SIR of 1.1 (range, 0.9–1.4). A potentially important future application for EB is studies of gene-environment-disease interactions, in which hundreds of polymorphisms may be evaluated with dozens of environmental risk factors in large cohort studies, producing thousands of associations.

This article has been cited by other articles:

				S. Greenland Multiple comparisons and association selection in general epidemiology Int. J. Epidemiol., June 1, 2008; 37(3): 430 - 434. [Full Text] [PDF]

				A t Mannetje, E Dryson, C Walls, D McLean, F McKenzie, M Maule, S Cheng, C Cunningham, H Kromhout, P Boffetta, et al. High risk occupations for non-Hodgkin's lymphoma in New Zealand: case-control study Occup. Environ. Med., May 1, 2008; 65(5): 354 - 363. [Abstract] [Full Text] [PDF]

				E. F. Kirrane, J. A. Hoppin, F. Kamel, D. M. Umbach, W. K. Boyes, A. J. DeRoos, M. Alavanja, and D. P. Sandler Retinal Degeneration and Other Eye Disorders in Wives of Farmer Pesticide Applicators Enrolled in the Agricultural Health Study Am. J. Epidemiol., June 1, 2005; 161(11): 1020 - 1029. [Abstract] [Full Text] [PDF]

				M. N. Cook, A. F. Olshan, H. A. Guess, D. A. Savitz, C. Poole, J. Blatt, M. L. Bondy, and B. H. Pollock Maternal Medication Use and Neuroblastoma in Offspring Am. J. Epidemiol., April 15, 2004; 159(8): 721 - 731. [Abstract] [Full Text] [PDF]

				T. Frudakis, M. Thomas, Z. Gaskin, K. Venkateswarlu, K. S. Chandra, S. Ginjupalli, S. Gunturi, S. Natrajan, V. K. Ponnuswamy, and K. N. Ponnuswamy Sequences Associated With Human Iris Pigmentation Genetics, December 1, 2003; 165(4): 2071 - 2083. [Abstract] [Full Text] [PDF]

				A J De Roos, S H Zahm, K P Cantor, D D Weisenburger, F F Holmes, L F Burmeister, and A Blair Integrative assessment of multiple pesticides as risk factors for non-Hodgkin's lymphoma among men Occup. Environ. Med., September 1, 2003; 60(9): e11 - 11. [Abstract] [Full Text] [PDF]