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Analysis of ordinal outcomes with longitudinal covariates subject to missingness

Author

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  • Melody S. Goodman
  • Yi Li
  • Anne M. Stoddard
  • Glorian Sorensen

Abstract

We propose a mixture model for data with an ordinal outcome and a longitudinal covariate that is subject to missingness. Data from a tailored telephone delivered, smoking cessation intervention for construction laborers are used to illustrate the method, which considers as an outcome a categorical measure of smoking cessation, and evaluates the effectiveness of the motivational telephone interviews on this outcome. We propose two model structures for the longitudinal covariate, for the case when the missing data are missing at random, and when the missing data mechanism is non-ignorable. A generalized EM algorithm is used to obtain maximum likelihood estimates.

Suggested Citation

  • Melody S. Goodman & Yi Li & Anne M. Stoddard & Glorian Sorensen, 2014. "Analysis of ordinal outcomes with longitudinal covariates subject to missingness," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(5), pages 1040-1052, May.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1040-1052
    DOI: 10.1080/02664763.2013.859236
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    References listed on IDEAS

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    1. Charles G. Minard & Wenyaw Chan & David W. Wetter & Carol J. Etzel, 2012. "Trends in smoking cessation: a Markov model approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 113-127, March.
    2. J. A. Anderson & P. R. Philips, 1981. "Regression, Discrimination and Measurement Models for Ordered Categorical Variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(1), pages 22-31, March.
    3. Paul S. Albert, 2000. "A Transitional Model for Longitudinal Binary Data Subject to Nonignorable Missing Data," Biometrics, The International Biometric Society, vol. 56(2), pages 602-608, June.
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