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A Factor Analysis Approach for Clustering Patient Reported Outcomes.

Jung Hun Oh1, Maria Thor, Caroline Olsson

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This study introduces a new computational method to identify clinically relevant patient-reported outcome symptom groups after radiation therapy. The method reveals significant correlations between radiation dose and these symptom groups, improving understanding of treatment side effects.

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Confirmatory factor analysisexploratory factor analysisfactor analysispatient reported outcomesradiotherapytoxicity

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Area of Science:

  • Radiation Oncology
  • Biostatistics
  • Cancer Patient Outcomes

Background:

  • Patient-reported outcomes (PROs) are crucial for measuring adverse side effects in radiation oncology.
  • Factor analysis (FA) can identify latent symptom groups, but linking them to treatment variables like radiation dose is key for quantitative analysis.
  • Identifying clinically relevant symptom groups and response variables is essential for understanding treatment response.

Purpose of the Study:

  • To develop a computational method for identifying clinically relevant symptom groups from PROs.
  • To test associations between identified symptom groups and radiation dose parameters.

Main Methods:

  • A novel approach combining exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to determine the optimal number of symptom groups.
  • Utilizing a combination of symptoms within identified groups as new response variables in linear regression.
  • Investigating relationships between these new response variables and dose-volume parameters.

Main Results:

  • Analysis of gastrointestinal symptom profiles from three prostate cancer radiotherapy datasets.
  • Validation of the EFA-CFA structural model across datasets, outperforming other FA methods.
  • Discovery of statistically significant correlations between dose-volume variables and identified symptom groups, enabling quantitative analysis.

Conclusions:

  • The proposed EFA-CFA method enhances the understanding of patient-reported outcomes in radiation oncology.
  • This approach provides a foundation for improved quantitative analysis of radiation-induced side effects.