Background: Survival bias is the phenomenon by which individuals are excluded from analysis of a trait because of mortality related to the expression of that trait. In genetic association studies, variants increasing risk for disease onset as well as risk of disease-related mortality (lethality) could be difficult to detect in genetic association case-control designs, possibly leading to underestimation of a variant's effect on disease risk. Methods and Results: We modeled cohorts for 3 diseases of high lethality (intracerebral hemorrhage, ischemic stroke, andmyocardial infarction) using existing longitudinal data. Based on these models, we simulated case-control genetic association studies for genetic risk factors of varying effect sizes, lethality, and minor allele frequencies. For each disease, erosion of detected effect size was larger for case-control studies of individuals of advanced age (age >75 years) and/or variants with very high event-associated lethality (genotype relative risk for event-related death >2.0). We found that survival bias results in no more than 20% effect size erosion for cohorts with mean age <75 years, even for variants that double lethality risk. Furthermore, we found that increasing effect size erosion was accompanied by depletion of minor allele frequencies in the case population, yielding a " signature" of the presence of survival bias. Conclusions: Our simulation provides formulas to allow estimation of effect size erosion given a variant's odds ratio of disease, odds ratio of lethality, and minor allele frequencies. These formulas will add precision to power calculation and replication efforts for case-control genetic studies. Our approach requires validation using prospective data.
- Myocardial infarction
ASJC Scopus subject areas
- Cardiology and Cardiovascular Medicine