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Factor Analytic Models of Clustered Multivariate Data with Informative Censoring

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  • David B. Dunson
  • Sally D. Perreault

Abstract

Summary. This article describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster‐level latent variables related to the primary outcomes and to the censoring process, and we account for dependency between these latent variables through a hierarchical model. A linear model is used to relate covariates and latent variables to the primary outcomes for each subunit. A generalized linear model accounts for covariate and latent variable effects on the probability of censoring for subunits within each cluster. The model accounts for correlation within clusters and within subunits through a flexible factor analytic framework that allows multiple latent variables and covariate effects on the latent variables. The structure of the model facilitates implementation of Markov chain Monte Carlo methods for posterior estimation. Data from a spermatotoxicity study are analyzed to illustrate the proposed approach.

Suggested Citation

  • David B. Dunson & Sally D. Perreault, 2001. "Factor Analytic Models of Clustered Multivariate Data with Informative Censoring," Biometrics, The International Biometric Society, vol. 57(1), pages 302-308, March.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:1:p:302-308
    DOI: 10.1111/j.0006-341X.2001.00302.x
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    References listed on IDEAS

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    1. D. B. Dunson & C. R. Weinberg & S. D. Perreault & R. E. Chapin, 1999. "Summarizing the Motion of Self-Propelled Cells: Applications to Sperm Motility," Biometrics, The International Biometric Society, vol. 55(2), pages 537-543, June.
    2. Irini Moustaki & Martin Knott, 2000. "Generalized latent trait models," Psychometrika, Springer;The Psychometric Society, vol. 65(3), pages 391-411, September.
    3. D. B. Dunson & G. E. Dinse, 2001. "Bayesian incidence analysis of animal tumorigenicity data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 125-141.
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    1. Chan, Jennifer S.K. & Leung, Doris Y.P. & Boris Choy, S.T. & Wan, Wai Y., 2009. "Nonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4530-4545, October.
    2. Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
    3. David B. Dunson & Zhen Chen & Jean Harry, 2003. "A Bayesian Approach for Joint Modeling of Cluster Size and Subunit-Specific Outcomes," Biometrics, The International Biometric Society, vol. 59(3), pages 521-530, September.
    4. Lu, Zudi & Zhang, Wenyang, 2012. "Semiparametric likelihood estimation in survival models with informative censoring," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 187-211.

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