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Score test for missing at random or not under logistic missingness models.

Hairu Wang1, Zhiping Lu1, Yukun Liu1

  • 1KLATASDS - MOE, School of Statistics, East China Normal University, Shanghai, China.

Biometrics
|March 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces novel score tests to distinguish between missing at random (MAR) and missing not at random (MNAR) data mechanisms. These tests effectively address model identification challenges, offering reliable statistical approaches for handling missing data.

Keywords:
missing at randommissing not at randomscore test

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Missing data are common across disciplines, categorized as missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).
  • Accurate identification of the missingness mechanism is vital for valid statistical analysis.
  • Existing research extensively covers MCAR vs. MAR testing, but MAR vs. MNAR testing remains underexplored, posing significant challenges due to model identification issues under MNAR.

Purpose of the Study:

  • To develop and evaluate statistical tests for differentiating between MAR and MNAR missingness mechanisms.
  • To address the critical challenge of model identification inherent in MNAR data.

Main Methods:

  • Development of two score tests based on a logistic model for missing probability.
  • Application of tests under both parametric and semiparametric location models for the regression function.
  • Circumvention of the model identification issue by relying solely on parameter estimation under the null MAR hypothesis.

Main Results:

  • The proposed score tests demonstrate well-controlled Type I errors in simulations.
  • The tests exhibit desirable statistical power for detecting MAR vs. MNAR.
  • Successful application illustrated through analysis of human immunodeficiency virus data.

Conclusions:

  • The developed score tests provide a robust method for distinguishing between MAR and MNAR data.
  • These tests offer a practical solution to the identification problem in MNAR data analysis.
  • The findings contribute to more reliable statistical inference in the presence of missing data.