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Summary
This summary is machine-generated.

Instrumental variables (IVs) can help identify missing data mechanisms, even when data are missing not at random (MNAR). This study provides conditions and methods for estimating population means with MNAR data using IVs.

Keywords:
Doubly robustInstrumental variableInverse probability weightingMissing not at random

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Missing data are prevalent in health and social sciences, hindering accurate inferences.
  • Missing not at random (MNAR) data poses significant challenges for standard statistical methods.
  • Instrumental variables (IVs) offer a potential solution when direct identification is impossible.

Purpose of the Study:

  • To establish necessary and sufficient conditions for nonparametric identification of data distributions under MNAR using IVs.
  • To develop semiparametric estimators for population outcome means with MNAR data.
  • To illustrate the application of these methods in a real-world scenario involving HIV seroprevalence.

Main Methods:

  • Derivation of necessary and sufficient conditions for identification under MNAR with IVs.
  • Development of inverse probability weighted, outcome regression, and doubly robust estimators.
  • Application of methods to HIV testing refusal data in Botswana.

Main Results:

  • Provided theoretical conditions for nonparametric identification of the full data distribution under MNAR with IVs.
  • Developed and presented a suite of semiparametric estimators for population means.
  • Demonstrated the practical utility of the methods in addressing selection bias in HIV seroprevalence studies.

Conclusions:

  • Instrumental variables can facilitate identification of data distributions under MNAR.
  • The proposed semiparametric estimators provide valid inference for population means with MNAR data.
  • The methodology offers a robust approach to handling selection bias in empirical research.