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Related Experiment Video

Updated: Nov 13, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Do Missing Values Influence Outcomes in a Cross-sectional Mail Survey?

Paul J Novotny1, Darrell Schroeder1, Jeff A Sloan1

  • 1Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.

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|March 15, 2021
PubMed
Summary
This summary is machine-generated.

Missing and inconsistent data in weight management surveys did not significantly alter study outcomes. Various data imputation techniques confirmed the original findings, ensuring reliable results for obesity research.

Keywords:
BMI, body mass indexMAR, missing at randomMCAR, missing completely at randomMCMC, Markov chain Monte CarloMNAR, missing not at randomOR, odds ratio

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Area of Science:

  • Health Sciences
  • Medical Informatics
  • Public Health

Background:

  • Weight management programs often rely on self-reported data, which can be subject to missing or inconsistent entries.
  • Ensuring data integrity is crucial for accurate analysis and effective intervention strategies in health research.

Purpose of the Study:

  • To evaluate the impact of missing and inconsistent body mass index (BMI) data on the results of a weight management mail survey.
  • To assess the robustness of study findings across various data imputation methods.

Main Methods:

  • Weight management surveys were distributed to overweight and obese individuals.
  • Analyses were conducted on complete datasets, datasets with invalid BMI values removed, and datasets with low BMI values treated as missing.
  • Multiple imputation techniques including expectation-maximization, Markov chain Monte Carlo, random forest, and multivariate imputation by chained equations were employed.

Main Results:

  • Approximately 8% of surveys had missing BMI values and 6% had invalid BMI values, with 26% missing at least one essential variable.
  • Imputation methods consistently identified BMI as a significant correlate of demographic factors like age, sex, race, marital status, and education.
  • Higher BMI was associated with increased feelings of being judged, disrespected, and treated unequally.

Conclusions:

  • The study concluded that missing data, even when addressed with various imputation methods, did not substantially affect the overall conclusions of the weight management survey.
  • The findings suggest that the original published results are reliable despite the presence of data imperfections.