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Test the reliability of doubly robust estimation with missing response data.

Baojiang Chen1, Jing Qin

  • 1Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska 68198, U.S.A.

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Summary
This summary is machine-generated.

This study introduces a new goodness-of-fit test for doubly robust (DR) estimates, crucial for handling missing data in statistical inference. The proposed test effectively checks regression model reliability and detects misspecification, improving data analysis in medical and social studies.

Keywords:
AuxiliaryDoubly robustEstimating equationGoodness of fitMissing data

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Regression model misspecification can bias statistical inference.
  • Missing data is a common challenge in social and medical research.
  • Doubly robust (DR) estimates offer robustness to model misspecification for missing data.

Purpose of the Study:

  • To develop a goodness-of-fit test for DR estimates with missing responses.
  • To assess the reliability of estimators derived from DR estimating equations.
  • To provide a method for diagnosing model misspecification in the presence of missing data.

Main Methods:

  • Proposed a novel testing method for DR estimating equations.
  • Utilized always observed auxiliary variables alongside possibly missing responses.
  • Conducted numerical studies to evaluate the test's performance.

Main Results:

  • The proposed test effectively controls type I errors.
  • The method demonstrates power in detecting departures from marginal mean model assumptions.
  • A real dementia dataset illustrated the test's application for model misspecification.

Conclusions:

  • The developed goodness-of-fit test enhances the reliability of DR estimates.
  • This method is valuable for ensuring accurate statistical inference with missing data.
  • The approach is applicable to cross-sectional data with missing outcomes and observed covariates.