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Goodman MS, Li Y, Stoddard AM, et al. Analysis of Ordinal Outcomes with Longitudinal Covariates Subject to Missingness. J Appl Stat. 2014;41(5):1040-1052doi: 10.1080/02664763.2013.859236.
Goodman, M. S., Li, Y., Stoddard, A. M., & Sorensen, G. (2014). Analysis of Ordinal Outcomes with Longitudinal Covariates Subject to Missingness. Journal of applied statistics, 41(5), 1040-1052. https://doi.org/10.1080/02664763.2013.859236
Goodman, Melody S, et al. "Analysis of Ordinal Outcomes with Longitudinal Covariates Subject to Missingness." Journal of applied statistics vol. 41,5 (2014): 1040-1052. doi: https://doi.org/10.1080/02664763.2013.859236
Goodman MS, Li Y, Stoddard AM, Sorensen G. Analysis of Ordinal Outcomes with Longitudinal Covariates Subject to Missingness. J Appl Stat. 2014 Jan 01;41(5):1040-1052. doi: 10.1080/02664763.2013.859236. PMID: 24791038; PMCID: PMC4002054.
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J Appl Stat. 2014 Jan 01;41(5):1040-1052. doi: 10.1080/02664763.2013.859236.

Analysis of Ordinal Outcomes with Longitudinal Covariates Subject to Missingness.

Journal of applied statistics

Melody S Goodman, Yi Li, Anne M Stoddard, Glorian Sorensen

Affiliations

  1. Division of Public Health Sciences, Department of Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, U.S.A.
  2. Department of Biostatistics, University of Michigan School of Public Health, Anne Arbor, MI, U.S.A.
  3. Center for Statistical Analysis & Research, New England Research Institute, Watertown, MA, U.S.A.
  4. Center for Community Based Research, Dana Farber Cancer Institute, Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, MA, U.S.A.

PMID: 24791038 PMCID: PMC4002054 DOI: 10.1080/02664763.2013.859236

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.

Keywords: longitudinal covariates; missingness; ordinal outcomes

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