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Manuel Gomes1, Rosalba Radice2, Jose Camarena Brenes2

  • 1Department of Applied Health Research, University College London, London, UK.

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Summary

This study introduces a flexible copula-based selection model to address missing not at random (MNAR) data challenges in statistical inference. The approach improves upon traditional methods by accommodating various outcome distributions for more robust analysis.

Keywords:
copulamissing not at randommultiple imputationnon-Gaussian outcomesselection modelsimultaneous equation modeling

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Missing data, particularly missing not at random (MNAR), presents significant challenges in statistical inference.
  • Traditional selection models often assume bivariate normality, limiting their applicability.
  • Existing robust methods are often restricted to specific joint distributions.

Purpose of the Study:

  • To introduce a flexible copula-based selection approach for handling MNAR data.
  • To propose a flexible imputation procedure compatible with the copula selection model.
  • To evaluate the performance of the copula model against standard selection models for estimating average treatment effects.

Main Methods:

  • Developed a copula-based selection model accommodating non-Gaussian outcome distributions and flexible functional forms.
  • Proposed a flexible imputation procedure generating values from the copula selection model.
  • Conducted a simulation study to compare the copula model with existing selection models.
  • Illustrated the methodology using data from the REFLUX study on laparoscopic surgery outcomes.

Main Results:

  • The copula-based selection approach offers greater flexibility in modeling outcome and selection equations compared to traditional methods.
  • The proposed imputation procedure generates plausible values, enhancing the robustness of the analysis.
  • Simulation results demonstrate the relative performance of the copula model for estimating average treatment effects with MNAR data.

Conclusions:

  • The copula-based selection framework provides a powerful and flexible tool for statistical inference with MNAR data.
  • The proposed methods are applicable to a wide range of non-Gaussian data and complex relationships.
  • Available R package GJRM facilitates the implementation of the copula selection model.