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Methods for shortening patient-reported outcome measures.

Daphna Harel1, Murray Baron2

  • 1Department of Applied Statistics, Social Science, and Humanities, New York University, New York, NY, USA.

Statistical Methods in Medical Research
|August 22, 2018
PubMed
Summary

This study introduces Optimal Test Assembly using mixed integer programming to shorten lengthy patient-reported outcome measures. This method offers a reproducible way to create shorter instruments, reducing patient burden and improving data quality in clinical research.

Keywords:
Item response theorygeneralized partial credit modeloptimal test assemblypatient-reported outcome measureshortened form

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Area of Science:

  • Health Outcomes Research
  • Psychometrics
  • Clinical Trial Methodology

Background:

  • Patient-reported outcome measures (PROMs) are crucial for assessing patient experiences and treatment efficacy.
  • Current PROMs can be lengthy, leading to patient burden, increased costs, and potential data quality issues.
  • There is a need for reproducible methods to shorten PROMs effectively.

Purpose of the Study:

  • To propose and evaluate Optimal Test Assembly (OTA) using mixed integer programming as a method for shortening PROMs.
  • To compare OTA with the existing standard method of item selection based on item response theory (IRT) discrimination parameters.
  • To demonstrate the application of OTA in shortening a fatigue scale for Systemic Sclerosis patients.

Main Methods:

  • Mixed integer programming (MIP) was employed for Optimal Test Assembly (OTA).
  • The OTA method was compared against item selection based on high discrimination parameters from an item response theory (IRT) model.
  • The methods were applied to a fatigue scale used in Systemic Sclerosis patient cohorts.

Main Results:

  • Optimal Test Assembly (OTA) provides a systematic and reproducible approach to shortening patient-reported outcome measures.
  • The study demonstrates the feasibility of using mixed integer programming for efficient instrument reduction.
  • Application to a Systemic Sclerosis fatigue scale showed the practical utility of the OTA method.

Conclusions:

  • Optimal Test Assembly (OTA) offers a robust and reproducible alternative for shortening patient-reported outcome measures.
  • This methodology can help mitigate patient burden and enhance the efficiency of clinical research data collection.
  • The proposed method holds promise for developing concise and effective PROMs across various clinical applications.