Unknown

Dataset Information

0

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.

SUBMITTER: Lin WY 

PROVIDER: S-EPMC3283699 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

altmetric image

Publications

Presenting the uncertainties of odds ratios using empirical-Bayes prediction intervals.

Lin Wan-Yu WY   Lee Wen-Chung WC  

PloS one 20120221 2


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 i  ...[more]

Similar Datasets

| S-EPMC5159955 | biostudies-literature
| S-EPMC5565406 | biostudies-literature
| S-EPMC2778678 | biostudies-other
| S-EPMC1475799 | biostudies-other
| S-EPMC2536726 | biostudies-literature
2024-06-05 | GSE32030 | GEO
| S-EPMC5286518 | biostudies-literature
| S-EPMC6472625 | biostudies-literature
| S-EPMC5510725 | biostudies-literature
| S-EPMC6529204 | biostudies-literature