4.6 Article

Determining the minimal clinically important difference criteria for the Multidimensional Fatigue Inventory in a radiotherapy population

Journal

SUPPORTIVE CARE IN CANCER
Volume 18, Issue 3, Pages 307-315

Publisher

SPRINGER
DOI: 10.1007/s00520-009-0653-z

Keywords

Cancer-related fatigue; Minimal clinically important difference; Multidimensional fatigue inventory; Utility; Anchor-based methods; Distribution-based methods

Funding

  1. Queensland Health Cancer Clinical Network
  2. Princess Alexandra Hospital Cancer Collaborative Group

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The Multidimensional Fatigue Inventory (MFI) is a commonly used cancer-related fatigue assessment tool. Unlike other fatigue assessments, there are no published minimal clinically important difference (MCID) criteria for the MFI in cancer populations. MCID criteria determine the smallest change in scores that can be regarded as important, allowing clinicians and researchers to interpret the meaning of changes in patient's fatigue scores. This research aims to improve the clinical utility of the MFI by establishing MCID criteria for the MFI sub-scales in a radiotherapy population. Two hundred ten patients undergoing radiotherapy were recruited to a single-centre prospective cohort study. Patients were assessed at three time points, at the start of radiotherapy, the end of radiotherapy and 6 weeks after radiotherapy completion. Assessment consisted of four clinically relevant constructs: (1) treatment impact on fatigue, (2) health-related quality of life, (3) performance status and (4) occupational productivity. These constructs were used as external or anchor-based measures to determine MCIDs for each sub-scale of the MFI. Multiple MCIDs were identified, each from a different perspective based on the constructs cited. Researchers seeking to use a generic MCID may wish to use a two-point reference for each MFI sub-scale as it was consistent across the pre- and post-radiotherapy comparison and occupational productivity anchors. MCIDs validated in this study allow better interpretation of changes in MFI sub-scale scores and allow effect size calculations for determining sample size in future studies.

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