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Strategies for Bias Reduction in Estimation of Marginal Means with Data Missing at Random

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Optimization and Data Analysis in Biomedical Informatics

Part of the book series: Fields Institute Communications ((FIC,volume 63))

Abstract

Incomplete data are common in many fields of research, and interest often lies in estimating a marginal mean based on available information. This paper is concerned with the comparison of different strategies for estimating the marginal mean of a response when data are missing at random. We evaluate these methods based on the asymptotic bias, empirical bias and efficiency. We show that complete case analysis gives biased results when data are missing at random, but inverse probability weighted estimating equations (IPWEE) and a method based on the expected conditional mean (ECM) yield consistent estimators.. While these methods give estimators which behave similarly in the contexts studied they are based on quite different assumptions. The IPWEE approach requires analysts to specify a model for the missing data mechanism whereas the ECM approach requires a model for the distribution of auxiliary variables driving the missing data mechanism. The latter can be a challenge in practice, particularly when the covariates are of high dimension or are a mixture of continuous and categorical variables. The IPWEE approach therefore has considerable appeal in many practical settings.

Mathematics Subject Classification (2010): Primary 62H12, Secondary 62F10

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Author information

Authors and Affiliations

  1. Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, 68198, USA

    Baojiang Chen

  2. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada, N2L 3G1

    Richard J. Cook

Authors
  1. Baojiang Chen
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  2. Richard J. Cook
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Corresponding author

Correspondence to Baojiang Chen .

Editor information

Editors and Affiliations

  1. , Department of Industrial & Systems Engin, University of Florida, Weil Hall 401, Gainesville, 32611, Florida, USA

    Panos M. Pardalos

  2. , Department of Mathematics, University of Waterloo, University Avenue West 200, Waterloo, N2L 3G1, Ontario, Canada

    Thomas F. Coleman

  3. , Department of Industrial Engineering, University of Central Florida, Central Florida Blvd 4000, Orlando, 32816, Florida, USA

    Petros Xanthopoulos

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Chen, B., Cook, R.J. (2013). Strategies for Bias Reduction in Estimation of Marginal Means with Data Missing at Random. In: Pardalos, P., Coleman, T., Xanthopoulos, P. (eds) Optimization and Data Analysis in Biomedical Informatics. Fields Institute Communications, vol 63. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4133-5_5

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