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Regression analysis for a summed missing data problem under an outcome‐dependent sampling scheme |
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| Authors: | Jieli Ding Yanyan Liu David B. Peden Steven R. Kleeberger Haibo Zhou |
| Affiliation: | 1. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;2. School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei 430072, China;3. Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;4. The Center for Environmental Medicine, Asthma, and Lung Biology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;5. National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC 27709, USA |
| Abstract: | In this paper, we consider a regression analysis for a missing data problem in which the variables of primary interest are unobserved under a general biased sampling scheme, an outcome‐dependent sampling (ODS) design. We propose a semiparametric empirical likelihood method for accessing the association between a continuous outcome response and unobservable interesting factors. Simulation study results show that ODS design can produce more efficient estimators than the simple random design of the same sample size. We demonstrate the proposed approach with a data set from an environmental study for the genetic effects on human lung function in COPD smokers. The Canadian Journal of Statistics 40: 282–303; 2012 © 2012 Statistical Society of Canada |
| Keywords: | Biased sampling empirical likelihood missing data semiparametric MSC 2010: Primary 62D05 secondary 62J99 |