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Missing Data in Patient-Reported Outcomes Research: Utilizing Multiple Imputation to Address an Unavoidable Problem.

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This summary is machine-generated.

Multiple imputation (MI) can address missing data in patient-reported outcomes (PROs) research. This study found MI provided consistent results in breast reconstruction, unlike analyses excluding missing data.

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Area of Science:

  • Plastic Surgery
  • Biostatistics
  • Health Outcomes Research

Background:

  • Patient-reported outcomes (PROs) are crucial in postoperative surgical care.
  • Missing data in PRO studies can introduce bias and affect conclusions.
  • Multiple imputation (MI) is a statistical technique to handle missing data.

Purpose of the Study:

  • To evaluate the utility of MI for addressing missing data in PRO research.
  • To compare MI results with complete case analysis in a real-world PRO dataset.

Main Methods:

  • Utilized the BREAST-Q PRO measure in a retrospective review of 916 postmastectomy reconstruction patients (2019).
  • Compared physical well-being scores between prepectoral and subpectoral cohorts at 2 weeks postoperatively.
  • Assessed the impact of increasing missing data scenarios on MI results.

Main Results:

  • Complete case analysis showed higher scores for prepectoral patients.
  • With increasing missingness, complete case analysis trends reversed, favoring subpectoral patients.
  • MI consistently indicated higher scores for prepectoral patients across all missingness scenarios.

Conclusions:

  • MI yields consistent results in PRO research, even with substantial missing data.
  • Analysis without MI can produce unreliable results when data is missing.
  • Highlights the importance of employing robust missing data methodologies in PRO studies.