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Nonrespondent Subsample Multiple Imputation in Two-Phase Sampling for Nonresponse

Author

Listed:
  • Zhang Nanhua

    (Division of Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, OH 45229, United States of America)

  • Chen Henian

    (Department of Epidemiology & Biostatistics, College of Public Health, University of South Florida, Tampa, FL 33612-3085, United States of America)

  • Elliott Michael R.

    (Department of Biostatistics, School of Public Health, University of Michigan and Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48019, United States of America)

Abstract

Nonresponse is very common in epidemiologic surveys and clinical trials. Common methods for dealing with missing data (e.g., complete-case analysis, ignorable-likelihood methods, and nonignorable modeling methods) rely on untestable assumptions. Nonresponse two-phase sampling (NTS), which takes a random sample of initial nonrespondents for follow-up data collection, provides a means to reduce nonresponse bias. However, traditional weighting methods to analyze data from NTS do not make full use of auxiliary variables. This article proposes a method called nonrespondent subsample multiple imputation (NSMI), where multiple imputation (Rubin 1987) is performed within the subsample of nonrespondents in Phase I using additional data collected in Phase II. The properties of the proposed methods by simulation are illustrated and the methods applied to a quality of life study. The simulation study shows that the gains from using the NTS scheme can be substantial, even if NTS sampling only collects data from a small proportion of the initial nonrespondents.

Suggested Citation

  • Zhang Nanhua & Chen Henian & Elliott Michael R., 2016. "Nonrespondent Subsample Multiple Imputation in Two-Phase Sampling for Nonresponse," Journal of Official Statistics, Sciendo, vol. 32(3), pages 769-785, September.
    • Handle: RePEc:vrs:offsta:v:32:y:2016:i:3:p:769-785:n:11
    DOI: 10.1515/jos-2016-0039
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    References listed on IDEAS

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    2. Nandram, Balgobin & Choi, Jai Won, 2010. "A Bayesian Analysis of Body Mass Index Data From Small Domains Under Nonignorable Nonresponse and Selection," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 120-135.
    3. Roderick J. Little & Nanhua Zhang, 2011. "Subsample ignorable likelihood for regression analysis with missing data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(4), pages 591-605, August.
    4. Nandram B. & Choi J.W., 2002. "Hierarchical Bayesian Nonresponse Models for Binary Data From Small Areas With Uncertainty About Ignorability," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 381-388, June.
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