Maximum likelihood inference on a mixed conditionally and marginally specified regression model for genetic epidemiologic studies with two-phase sampling

• Published in: Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 69; no. 2; pp. 123 - 142
• Main Authors: Chatterjee, Nilanjan, Chen, Yi-Hau
• Format: Journal Article
• Language: English
• Published: Oxford, UK Oxford, UK : Blackwell Publishing Ltd 01.04.2007; Blackwell Publishing Ltd; Blackwell Publishers; Blackwell; Royal Statistical Society; Oxford University Press
• Series: Journal of the Royal Statistical Society Series B
• Subjects: adenoma; animal ovaries; Applications; Biology, psychology, social sciences; Cancer; Case control studies; cost effectiveness; Data analysis; Data collection; Disease models; Disease risk; Environment; environmental exposure; environmental factors; Epidemiologic studies; Epidemiology; Estimation methods; Exact sciences and technology; Family history; Gene-environment interaction; genetic variation; Genetics; Health; Human genetics; Mathematics; Maximum likelihood estimation; Medical genetics; Medical sciences; Medical screening; Missing data; Nonparametric inference; Outcome-dependent sampling; ovarian neoplasms; Parametric models; Probability and statistics; Regression analysis; risk; Sampling theory, sample surveys; Sciences and techniques of general use; screening; Semiparametric methods; Statistical methods; Statistics; Studies; Outcome- dependent sampling; Data analysis; Conditional distribution; Family study; Gene-environment interaction; Statistical estimation; Case control study; Semiparametric method; Epidemiology; Gene environment interaction; Statistical method; Missing data; Risk estimation; Regression model; Genetics; Case-control studies; Asymptotic approximation; Maximum likelihood; Outcome-dependent sampling; Semiparametric methods
• ISSN: 1369-7412; 1467-9868
• DOI: 10.1111/j.1467-9868.2007.00580.x

Two-phase stratified sampling designs can reduce the cost of genetic epidemiologic studies by limiting expensive ascertainments of genetic and environmental exposure to an efficiently selected subsample (phase II) of the main study (phase I). Family history and some covariate information, which may be cheaply gathered for all subjects at phase I, can be used for sampling of informative subjects at phase II. We develop alternative maximum likelihood methods for analysis of data from such studies by using a novel regression model that permits the estimation of 'marginal' risk parameters that are associated with the genetic and environmental covariates of interest, while simultaneously characterizing the 'conditional' risk of the disease associated with family history after adjusting for the other covariates. The methods and appropriate asymptotic theories are developed with and without an assumption of gene-environment independence, allowing the distribution of the environmental factors to remain non-parametric. The performance of the alternative methods and of sampling strategies is studied by using simulated data involving rare and common genetic variants. An application of the methods proposed is illustrated by using a case-control study of colorectal adenoma embedded within the prostate, lung, colorectal and ovarian cancer screening trial.