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Semiparametric models for missing covariate and response data in regression models.

Qingxia Chen1, Joseph G Ibrahim

  • 1Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37232, USA. cindy.chen@vanderbilt.edu

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Missing Data Analysis

Background:

  • Missing covariate and response data are common in regression analyses.
  • Existing methods may be sensitive to misspecification of the missing data mechanism.
  • Generalized linear models and mixed models are widely used but can be challenged by missing data.

Purpose of the Study:

  • To propose semiparametric models for handling missing covariate and/or response data.
  • To provide a sensitivity analysis framework for model misspecification.
  • To develop robust methods for generalized linear models and generalized linear mixed models.

Main Methods:

  • Utilized semiparametric models for covariate distribution and missing data mechanisms.
  • Employed generalized additive models (GAMs) for covariate distribution and/or missing data mechanism.
  • Applied penalized regression splines to express GAMs as generalized linear mixed effects models.
  • Obtained maximum likelihood estimates using the Expectation-Maximization (EM) algorithm.

Main Results:

  • The proposed semiparametric model serves as a sensitivity analysis for potential misspecification.
  • The variance of random effects in the penalized spline GAM offers an index for model selection.
  • Simulations demonstrated the effectiveness of the proposed methodology.
  • The methods were applied to a melanoma cancer clinical trial dataset.

Conclusions:

  • The developed semiparametric approach offers a flexible and robust method for analyzing data with missing covariates or responses.
  • This framework enhances the reliability of statistical inferences in the presence of missing data.
  • The methodology provides valuable tools for biostatistical analysis, particularly in clinical trial settings.