Condition-specific measure was more responsive than generic measure in colorectal cancer: all but social domains

J Clin Epidemiol. 2013 May;66(5):557-65. doi: 10.1016/j.jclinepi.2012.11.010. Epub 2013 Feb 8.

Abstract

Objective: To examine the responsiveness of generic and condition-specific instruments based on the anchor of self-reported level of global change in patients with colorectal cancer (CRC).

Study design and setting: Three hundred thirty-three patients with CRC were surveyed at two assessments at baseline and follow-up at 6 months from September 2009 to July 2010 using the Short Form-12 Health Survey version 2 (SF-12v2) and Functional Assessment of Cancer Therapy-Colorectal (FACT-C) measures. The responsiveness of the two measures was evaluated using standardized effect size, standardized response mean, responsiveness statistic, and receiver operating characteristic (ROC) curve analysis.

Results: In worsened group, internal responsiveness of detecting negative changes was satisfactory for most subscales of FACT-C and SF-12v2. The FACT-C subscales were significantly more responsive to positive changes detection than the SF-12v2 subscales in improved group. Physical well-being subscale, Trial Outcome Index (TOI), and total score of FACT-C were more externally responsive to ROC curve analysis. The FACT-C measure was generally more responsive to changes in health status compared with SF-12v2 measure.

Conclusion: TOI and total score of FACT-C were the most responsive among subscales of condition-specific measure, which were more responsive than all generic subscales with the exception of social domain. Complementary use of condition-specific and generic instruments to evaluate the health-related quality of life of CRC patients is encouraged.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adaptation, Physiological
  • Aged
  • China
  • Colorectal Neoplasms / diagnosis
  • Colorectal Neoplasms / psychology*
  • Colorectal Neoplasms / therapy
  • Female
  • Health Surveys
  • Humans
  • Male
  • Middle Aged
  • Patient Satisfaction / statistics & numerical data*
  • Quality of Life*
  • ROC Curve
  • Risk Factors
  • Sickness Impact Profile*
  • Socioeconomic Factors
  • Surveys and Questionnaires*