Related Experiment Videos
Computing differential sample size for case-control studies of gene-environment interaction
Jimmy Thomas Efird1, Mi-Kyung Hong
1Biostatistics and Data Management Facility, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii 96822, USA. jimmy.efird@stanfordlumni.org
Abstract:
The rates for diseases such as cancer, cardiovascular disease, and diabetes are known to differ by ethnic/racial groups. However, neither genetic nor environmental factors fully explain the observed differences. Failure to account for genetic expression in the absence or presence of an environmental factor, and vice-versa, may lead to erroneous conclusions regarding the importance of these factors in disease etiology. We present a novel method for computing sample size for case-control studies involving the interaction of genetic and environmental factors. The method is based on an indirect estimate of the odds ratio for gene-environment interaction given only the odds ratio for environmental exposure and population genotype frequency. A table is presented providing sample sizes required for detecting a minimum odds ratio for gene-environment interaction given varying genotype frequencies and environmental exposure odds ratio values. Sample size increases proportionately with genotype frequency for a given environment exposure odds ratio.
Related Concept Videos
Gene-Environment Interactions
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Statistical Methods for Analyzing Epidemiological Data
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Sample Size Calculation
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Statistical Software for Data Analysis and Clinical Trials