Genetic effect estimates in case‐control studies when a continuous variable is omitted from the model
| العنوان: | Genetic effect estimates in case‐control studies when a continuous variable is omitted from the model |
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| المؤلفون: | Chiung Yu Huang, Iryna Lobach, Ying Sheng, Lydia B. Zablotska, Siarhei Lobach |
| المصدر: | Genetic Epidemiology, vol 44, iss 3 |
| بيانات النشر: | Wiley, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | omitted continuous variable, bias, Epidemiology, case-control study, Logistic regression, 01 natural sciences, Continuous variable, 010104 statistics & probability, 03 medical and health sciences, Neuroimaging, Statistics, Genetics, 2.1 Biological and endogenous factors, odds ratio, Aetiology, 0101 mathematics, Genetics (clinical), Mathematics, 030304 developmental biology, 0303 health sciences, Human Genome, 030305 genetics & heredity, Case-control study, Omitted-variable bias, Conditional probability distribution, Odds ratio, Alzheimer's disease, Distribution (mathematics), Public Health and Health Services, False positive rate, Psychology |
| الوصف: | Large-scale genome-wide analyses scans provide massive volumes of genetic variants on large number of cases and controls that can be used to estimate the genetic effects. Yet, the sets of non-genetic variables available in publicly available databases are often brief. It is known that omitting a continuous variable from a logistic regression model can result in biased estimates of odds ratios (OR) (e.g., Gail et al (1984), Neuhaus et al (1993), Hauck et al (1991), Zeger et al (1988)). We are interested to assess what information is needed to recover the bias in the OR estimate of genotype due to omitting a continuous variable in settings when the actual values of the omitted variable are not available. We derive two estimating procedures that can recover the degree of bias based on a conditional density of the omitted variable or knowing the distribution of the omitted variable. Importantly, our derivations show that omitting a continuous variable can result in either under- or over-estimation of the genetic effects. We performed extensive simulation studies to examine bias, variability, false positive rate, and power in the model that omits a continuous variable. We show the application to two genome-wide studies of Alzheimer’s disease.Data Availability StatementThe data that support the findings of this study are openly available in the Database of Genotypes and Phenotypes at [https://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?study_id=phs000372.v1.p1], reference number [phs000372.v1.p1] and at the Alzheimer’s Disease Neuroimaging Initiative http://adni.loni.usc.edu/. |
| تدمد: | 1098-2272 0741-0395 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6bed84868f35b17981746c796701c72 https://doi.org/10.1002/gepi.22278 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....c6bed84868f35b17981746c796701c72 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10982272 07410395 |
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