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Logistic analysis of epidemiologic studies with augmentation sampling involving re-stratification and population expansion.

ABSTRACT: Epidemiologic cross-sectional, case-cohort, or case-control studies often select augmentation samples to supplement an existing (baseline) sample, primarily for the two reasons: (1) to increase the sample sizes from certain subdomains of interest that were not originally considered in the design of the baseline study and (2) to obtain samples from an extension of the target population. To address these two objectives, two-stage stratified sample designs are considered, where the stratification based on the expanded population at the second stage is not nested in the first stage strata. The sample weighting and Taylor linearization variance estimation for the two-stage stratified sample designs, involving re-stratification and population expansion, are provided for estimating population totals and logistic regression coefficients. Results from limited simulation studies and a logistic regression analysis of a study of human papillomavirus serology are provided.

SUBMITTER: Li Y

PROVIDER: S-EPMC4263221 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Logistic analysis of epidemiologic studies with augmentation sampling involving re-stratification and population expansion.

Li Yan Y Safaeian Mahboobeh M Robbins Hilary A HA Graubard Barry I BI

Biostatistics (Oxford, England) 20140606 1

Epidemiologic cross-sectional, case-cohort, or case-control studies often select augmentation samples to supplement an existing (baseline) sample, primarily for the two reasons: (1) to increase the sample sizes from certain subdomains of interest that were not originally considered in the design of the baseline study and (2) to obtain samples from an extension of the target population. To address these two objectives, two-stage stratified sample designs are considered, where the stratification b ...[more]

PMID: 24907707

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Project description:Population stratification or confounding by genetic ancestry is a potential cause of false associations in genetic association studies. Estimation of and adjustment for genetic ancestry has become common practice thanks in part to the availability of ancestry informative markers on genome-wide association study (GWAS) arrays. While array data is now widespread, these data are not ubiquitous as several large epidemiologic and clinic-based studies lack genome-wide data. One such large epidemiologic-based study lacking genome-wide data accessible to investigators is the National Health and Nutrition Examination Surveys (NHANES), population-based cross-sectional surveys of Americans linked to demographic, health, and lifestyle data conducted by the Centers for Disease Control and Prevention. DNA samples (n = 14,998) were extracted from biospecimens from consented NHANES participants between 1991-1994 (NHANES III, phase 2) and 1999-2002 and represent three major self-identified racial/ethnic groups: non-Hispanic whites (n = 6,634), non-Hispanic blacks (n = 3,458), and Mexican Americans (n = 3,950). We as the Epidemiologic Architecture for Genes Linked to Environment study genotyped candidate gene and GWAS-identified index variants in NHANES as part of the larger Population Architecture using Genomics and Epidemiology I study for collaborative genetic association studies. To enable basic quality control such as estimation of genetic ancestry to control for population stratification in NHANES san genome-wide data, we outline here strategies that use limited genetic data to identify the markers optimal for characterizing genetic ancestry. From among 411 and 295 autosomal SNPs available in NHANES III and NHANES 1999-2002, we demonstrate that markers with ancestry information can be identified to estimate global ancestry. Despite limited resolution, global genetic ancestry is highly correlated with self-identified race for the majority of participants, although less so for ethnicity. Overall, the strategies outlined here for a large epidemiologic study can be applied to other datasets accessible for genotype-phenotype studies but are sans genome-wide data.

Dataset Information

Logistic analysis of epidemiologic studies with augmentation sampling involving re-stratification and population expansion.

Publications

Logistic analysis of epidemiologic studies with augmentation sampling involving re-stratification and population expansion.

Similar Datasets

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