Robust inference for mixed censored and binary response models with missing covariates.

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  • Author(s): Sarkar A;Sarkar A; Das K; Das K; Sinha SK; Sinha SK
  • Source:
    Statistical methods in medical research [Stat Methods Med Res] 2016 Oct; Vol. 25 (5), pp. 1836-1853. Date of Electronic Publication: 2013 Oct 09.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: SAGE Publications Country of Publication: England NLM ID: 9212457 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1477-0334 (Electronic) Linking ISSN: 09622802 NLM ISO Abbreviation: Stat Methods Med Res Subsets: MEDLINE
    • Publication Information:
      Publication: London : SAGE Publications
      Original Publication: Sevenoaks, Kent, UK : Edward Arnold, c1992-
    • Subject Terms:
      Algorithms* ; Likelihood Functions* ; Monte Carlo Method*; Blood Glucose/analysis ; Diabetes Mellitus/blood ; Humans ; Probability
    • Abstract:
      In biomedical and epidemiological studies, often outcomes obtained are of mixed discrete and continuous in nature. Furthermore, due to some technical inconvenience or else, continuous responses are censored and also a few covariates cease to be observed completely. In this paper, we develop a model to tackle these complex situations. Our methodology is developed in a more general framework and provides a full-scale robust analysis of such complex models. The proposed robust maximum likelihood estimators of the model parameters are resistant to potential outliers in the data. We discuss the asymptotic properties of the robust estimators. To avoid computational difficulties involving irreducibly high-dimensional integrals, we propose a Monte Carlo method based on the Metropolis algorithm for approximating the robust maximum likelihood estimators. We study the empirical properties of these estimators in simulations. We also illustrate the proposed robust method using clustered data on blood sugar content from a clinical trial of individuals who were investigated for diabetes.
      (© The Author(s) 2013.)
    • Contributed Indexing:
      Keywords: binary model; censored regression model; expectation maximization algorithm; metropolis algorithm; missing data; robust estimation
    • Accession Number:
      0 (Blood Glucose)
    • Publication Date:
      Date Created: 20131011 Date Completed: 20180425 Latest Revision: 20181202
    • Publication Date:
      20221213
    • Accession Number:
      10.1177/0962280213503924
    • Accession Number:
      24108268