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A multiple imputation-based sensitivity analysis approach for data subject to missing not at random.

Chiu-Hsieh Hsu1, Yulei He2, Chengcheng Hu1

  • 1Department of Epidemiology and Biostatistics, College of Public Health, University of Arizona, Tucson, Arizona, USA.

Statistics in Medicine
|July 28, 2020
PubMed
Summary
This summary is machine-generated.

Sensitivity analysis for missing data is crucial when data is missing not at random. This study proposes a robust nonparametric multiple imputation method using a correlation coefficient for missing data analysis.

Keywords:
correlation coefficientmissing not at randommultiple imputationselection modelsensitivity analysis

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Missing data mechanisms are theoretically unverifiable from observed data alone.
  • Sensitivity analysis is essential to assess the impact of potential missing not at random (MNAR) mechanisms.
  • Existing sensitivity analysis methods often require full specification of the relationship between missing values and missingness probabilities.

Purpose of the Study:

  • To propose a novel sensitivity analysis approach for missing data under the selection modeling framework.
  • To introduce a nonparametric multiple imputation strategy that simplifies sensitivity analysis parameter specification.
  • To evaluate the robustness of the proposed approach against misspecifications of the sensitivity parameter.

Main Methods:

  • Developed a nonparametric multiple imputation strategy within the selection modeling framework.
  • Utilized the correlation coefficient between missing values and selection probabilities as the sensitivity parameter.
  • The sensitivity parameter is employed solely for selecting imputing/donor sets, enhancing robustness.

Main Results:

  • The proposed approach simplifies sensitivity analysis by requiring only the specification of a correlation coefficient.
  • The method demonstrated potential robustness against misspecifications of the sensitivity parameter.
  • Applied the approach to analyze preoperative Hemoglobin A1c levels in patients with carotid artery stenosis.

Conclusions:

  • The proposed nonparametric multiple imputation strategy offers a more robust and practical approach to sensitivity analysis for missing data.
  • This method facilitates the evaluation of MNAR mechanisms by focusing on a single, standardized parameter.
  • Further simulation studies confirmed the performance and utility of the developed approach in real-world data scenarios.