Nonrespondent Subsample Multiple Imputation in Two-Phase Sampling for Nonresponse

• Zhang, Nanhua ^[3] ; Chen, Henian ^[1] ; Elliott, Michael R. ^[2]
  1. [1] University of South Florida

     University of South Florida

     Estados Unidos

     
  2. [2] University of Michigan–Ann Arbor

     University of Michigan–Ann Arbor

     City of Ann Arbor, Estados Unidos

     
  3. [3] Cincinnati Children’s Hospital Medical Center

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• Localización: Journal of official statistics, ISSN 0282-423X, Vol. 32, Nº. 3, 2016, págs. 769-785
• Idioma: inglés
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• Resumen

  • 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.