Multiple robust estimation of marginal structural mean models for unconstrained outcomes.

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  • Author(s): Babino L;Babino L; Rotnitzky A; Rotnitzky A; Robins J; Robins J
  • Source:
    Biometrics [Biometrics] 2019 Mar; Vol. 75 (1), pp. 90-99. Date of Electronic Publication: 2018 Jul 13.
  • Publication Type:
    Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Biometric Society Country of Publication: United States NLM ID: 0370625 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
    • Publication Information:
      Publication: Alexandria Va : Biometric Society
      Original Publication: Washington.
    • Subject Terms:
      Data Interpretation, Statistical* ; Likelihood Functions* ; Models, Statistical* ; Regression Analysis*; Child ; Computer Simulation ; Diet ; Exercise ; Female ; Humans ; Obesity/ethnology ; Obesity/etiology
    • Abstract:
      We consider estimation, from longitudinal observational data, of the parameters of marginal structural mean models for unconstrained outcomes. Current proposals include inverse probability of treatment weighted and double robust (DR) estimators. A difficulty with DR estimation is that it requires postulating a sequence of models, one for the each mean of the counterfactual outcome given covariate and treatment history up to each exposure time point. Most natural models for such means are often incompatible. Robins et al., (2000b) proposed a parameterization of the likelihood which implies compatible parametric models for such means. Their parameterization has not been exploited to construct DR estimators and one goal of this article is to fill this gap. More importantly, exploiting this parameterization we propose a multiple robust (MR) estimator that confers even more protection against model misspecification than DR estimators. Our methods are easy to implement as they are based on the iterative fit of a sequence of weighted regressions.
      (© 2018, The International Biometric Society.)
    • Grant Information:
      DP1 ES025459 United States ES NIEHS NIH HHS; R01 AI112339 United States AI NIAID NIH HHS
    • Contributed Indexing:
      Keywords: Compatible models; Doubly robust estimation; Inverse probability weighted estimation
    • Publication Date:
      Date Created: 20180714 Date Completed: 20191216 Latest Revision: 20191217
    • Publication Date:
      20240829
    • Accession Number:
      10.1111/biom.12924
    • Accession Number:
      30004573