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Missing data methods in longitudinal studies: a review


  • Joseph Ibrahim


  • Geert Molenberghs



No abstract is available for this item.

Suggested Citation

  • Joseph Ibrahim & Geert Molenberghs, 2009. "Missing data methods in longitudinal studies: a review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 1-43, May.
  • Handle: RePEc:spr:testjl:v:18:y:2009:i:1:p:1-43 DOI: 10.1007/s11749-009-0138-x

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    References listed on IDEAS

    1. Chen, Qingxia & Ibrahim, Joseph G. & Chen, Ming-Hui & Senchaudhuri, Pralay, 2008. "Theory and inference for regression models with missing responses and covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1302-1331, July.
    2. Jane Xu & Scott L. Zeger, 2001. "Joint analysis of longitudinal data comprising repeated measures and times to events," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 375-387.
    3. Herring A. H & Ibrahim J. G, 2001. "Likelihood-Based Methods for Missing Covariates in the Cox Proportional Hazards Model," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 292-302, March.
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    Cited by:

    1. repec:bla:jorssa:v:180:y:2017:i:3:p:723-749 is not listed on IDEAS
    2. Antonello Maruotti, 2015. "Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 84-109, March.
    3. Cai, T. Tony & Zhang, Anru, 2016. "Minimax rate-optimal estimation of high-dimensional covariance matrices with incomplete data," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 55-74.


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