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Comments on: Missing data methods in longitudinal studies: a review

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References

  • Azzalini A (1994) Logistic regression for autocorrelated data with application to repeated measures. Biometrika 81(4):767–775

    Article  MATH  Google Scholar 

  • Daniels MJ, Hogan JW (2008) Missing data in longitudinal studies: strategies for Bayesian modeling and sensitivity analysis. Chapman & Hall/CRC, London/Boca Raton

    MATH  Google Scholar 

  • Fitzmaurice G, Laird N, Shneyer L (2001) An alternative parameterization of the general linear mixture model for longitudinal data with non-ignorable drop-outs. Stat Med 20(7):1009–1021

    Article  Google Scholar 

  • Heagerty P (1999) Marginally specified logistic-normal models for longitudinal binary data. Biometrics 55(3):688–698

    Article  MATH  MathSciNet  Google Scholar 

  • Heagerty P (2002) Marginalized transition models and likelihood inference for longitudinal categorical data. Biometrics 58(2):342–351

    Article  MathSciNet  Google Scholar 

  • Little RJA (1993) Pattern-mixture models for multivariate incomplete data. J Am Stat Assoc 88(421):125–134

    Article  MATH  Google Scholar 

  • Little RJA (1994) A class of pattern-mixture models for normal incomplete data. Biometrika 81(3):471–483

    Article  MATH  MathSciNet  Google Scholar 

  • Molenberghs G, Michiels B, Kenward M, Diggle P (1998) Monotone missing data and pattern-mixture models. Stat Neerl 52(2):153–161

    Article  MATH  MathSciNet  Google Scholar 

  • Rotnitzky A, Robins J, Scharfstein DO (1998) Semiparametric regression for repeated outcomes with nonignorable nonresponse. J Am Stat Assoc 93(444):1321–1322

    Article  MATH  MathSciNet  Google Scholar 

  • Roy J, Daniels MJ (2008) A general class of pattern mixture models for nonignorable dropout with many possible dropout times. Biometrics 64:538–545

    Article  MATH  Google Scholar 

  • Scharfstein DO, Daniels MJ, Robins J (2003) Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes. Biostatistics 4(4):495–512

    Article  MATH  Google Scholar 

  • Troxel A, Ma G, Heitjan D (2004) An index of local sensitivity to nonignorability. Stat Sin 14(4):1221–1238

    MATH  MathSciNet  Google Scholar 

  • Wang C, Daniels MJ (2009) A note on identifying restrictions in normal mixture models with and without covariates for incomplete data (working paper)

  • Wang C, Daniels MJ, Scharfstein DO (2009) Bayesian semiparametric selection models with application to a breast cancer prevention trial (working paper)

  • Wilkins K, Fitzmaurice G (2006) A hybrid model for nonignorable dropout in longitudinal binary responses. Biometrics 62(1):168–176

    Article  MATH  MathSciNet  Google Scholar 

  • Zhang J, Heitjan D (2006) A simple local sensitivity analysis tool for nonignorable coarsening: application to dependent censoring. Biometrics 62(4):1260–1268

    Article  MATH  MathSciNet  Google Scholar 

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  1. Department of Statistics, University of Florida, Gainesville, USA

    Michael J. Daniels & Chenguang Wang

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  1. Michael J. Daniels
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  2. Chenguang Wang
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Correspondence to Michael J. Daniels.

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This comment refers to the invited paper available at: http://dx.doi.org/10.1007/s11749-009-0138-x.

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Daniels, M.J., Wang, C. Comments on: Missing data methods in longitudinal studies: a review. TEST 18, 51–58 (2009). https://doi.org/10.1007/s11749-009-0141-2

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