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An R-Based Landscape Validation of a Competing Risk Model
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Symptom clusters in a population-based ambulatory cancer cohort validated using bootstrap methods.

Doris Howell1, Amna Husain, Hsien Seow

  • 1Ontario Cancer Institute and Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada. doris.howell@uhn.on.ca

European Journal of Cancer (Oxford, England : 1990)
|May 29, 2012
PubMed
Summary

Researchers identified three common symptom clusters in cancer patients: fatigue-sickness, emotional distress, and poor well-being. These findings aid in targeted symptom management and identifying at-risk populations.

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Area of Science:

  • Oncology
  • Symptom Research
  • Biostatistics

Background:

  • Symptom cluster identification is crucial for cancer patient care.
  • Varied statistical methods hinder consistent cluster identification.
  • A need exists for validated, common symptom clusters in ambulatory cancer patients.

Purpose of the Study:

  • To identify and validate common symptom clusters.
  • To utilize a large, population-based cohort of ambulatory cancer subjects.
  • To establish a foundation for improved symptom management interventions.

Main Methods:

  • Descriptive, factor analysis study design.
  • Bootstrap methods for stable factor structure derivation.
  • Confirmatory factor analysis for cluster validation in 14,247 subjects.

Main Results:

  • Three distinct symptom clusters were identified: fatigue-sickness, emotional distress, and poor sense of well-being.
  • Fatigue-sickness included tiredness, nausea, drowsiness, and shortness of breath.
  • Emotional distress encompassed depression and anxiety; poor well-being included appetite and general well-being.
  • Identified clusters demonstrated stability across diverse sub-populations.

Conclusions:

  • Robust statistical methods successfully identified common symptom clusters.
  • These validated clusters offer targets for improved symptom management strategies.
  • Identification aids in pinpointing patient populations at risk for adverse outcomes.