title: Presenting the uncertainties of odds ratios using empirical-Bayes prediction intervals.
abstract: Quantifying exposure-disease associations is a central issue in epidemiology. Researchers of a study often present an odds ratio (or a logarithm of odds ratio, logOR) estimate together with its confidence interval (CI), for each exposure they examined. Here the authors advocate using the empirical-Bayes-based 'prediction intervals' (PIs) to bound the uncertainty of logORs. The PI approach is applicable to a panel of factors believed to be exchangeable (no extra information, other than the data itself, is available to distinguish some logORs from the others). The authors demonstrate its use in a genetic epidemiological study on age-related macular degeneration (AMD). The proposed PIs can enjoy straightforward probabilistic interpretations--a 95% PI has a probability of 0.95 to encompass the true value, and the expected number of true values that are being encompassed is 0.95m for a total of m 95% PIs. The PI approach is theoretically more efficient (producing shorter intervals) than the traditional CI approach. In the AMD data, the average efficiency gain is 51.2%. The PI approach is advocated to present the uncertainties of many logORs in a study, for its straightforward probabilistic interpretations and higher efficiency while maintaining the nominal coverage probability.
authors.affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Wan-Yu
authors.initials: WY
authors.lastname: Lin
authors.forename: Wen-Chung
authors.initials: WC
authors.lastname: Lee
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D016001:Confidence Intervals; D006801:Humans; D008268:Macular Degeneration / Q000235:genetics*; D008957:Models, Genetic; D016017:Odds Ratio; D035501:Uncertainty