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Missing-data methods for generalized linear models: A comparative review

Publication ,  Journal Article
Ibrahim, JG; Chen, MH; Lipsitz, SR; Herring, AH
Published in: Journal of the American Statistical Association
March 1, 2005

Missing data is a major issue in many applied problems, especially in the biomedical sciences. We review four common approaches for inference in generalized linear models (GLMs) with missing covariate data: maximum likelihood (ML), multiple imputation (MI), fully Bayesian (FB), and weighted estimating equations (WEEs). There is considerable interest in how these four methodologies are related, the properties of each approach, the advantages and disadvantages of each methodology, and computational implementation. We examine data that are missing at random and nonignorable missing. For ML, we focus on techniques using the EM algorithm, and in particular, discuss the EM by the method of weights and related procedures as discussed by Ibrahim. For MI, we examine the techniques developed by Rubin. For FB, we review approaches considered by Ibrahim et al. For WEE, we focus on the techniques developed by Robins et al. We use a real dataset and a detailed simulation study to compare the four methods. © 2005 American Statistical Association.

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Published In

Journal of the American Statistical Association

DOI

ISSN

0162-1459

Publication Date

March 1, 2005

Volume

100

Issue

469

Start / End Page

332 / 346

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

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Ibrahim, J. G., Chen, M. H., Lipsitz, S. R., & Herring, A. H. (2005). Missing-data methods for generalized linear models: A comparative review. Journal of the American Statistical Association, 100(469), 332–346. https://doi.org/10.1198/016214504000001844
Ibrahim, J. G., M. H. Chen, S. R. Lipsitz, and A. H. Herring. “Missing-data methods for generalized linear models: A comparative review.” Journal of the American Statistical Association 100, no. 469 (March 1, 2005): 332–46. https://doi.org/10.1198/016214504000001844.
Ibrahim JG, Chen MH, Lipsitz SR, Herring AH. Missing-data methods for generalized linear models: A comparative review. Journal of the American Statistical Association. 2005 Mar 1;100(469):332–46.
Ibrahim, J. G., et al. “Missing-data methods for generalized linear models: A comparative review.” Journal of the American Statistical Association, vol. 100, no. 469, Mar. 2005, pp. 332–46. Scopus, doi:10.1198/016214504000001844.
Ibrahim JG, Chen MH, Lipsitz SR, Herring AH. Missing-data methods for generalized linear models: A comparative review. Journal of the American Statistical Association. 2005 Mar 1;100(469):332–346.
Journal cover image

Published In

Journal of the American Statistical Association

DOI

ISSN

0162-1459

Publication Date

March 1, 2005

Volume

100

Issue

469

Start / End Page

332 / 346

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics