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Interpretation

OF THE FACIT MEASURES

Due to the evolving nature of QOL research, the best approach to interpreting data collected with a FACIT measure is to conduct a comprehensive literature search to determine the approaches taken by others and build upon that body of work.

Development

Most FACIT measures have undergone a standard scale development and validation methodology, which takes place in four phases: item generation, item reduction, scale construction, and psychometric evaluation. The scale development process involves considerable input from patients and expert health care providers, using a semi-structured interview designed to elicit personal experiences and educated opinions about how a disease, treatment, or condition may affect physical status, emotional well-being, functional well-being, family/social issues, sexuality/intimacy, work status, and future orientation. This process yields an exhaustive list of candidate items, which then undergo a series of reviews and reductions based on patient and expert ratings and item quality. A finite set of targeted concerns are then derived. Final candidate items are formatted with response choices compatible with a 5-point Likert-type scale, and appended to the FACT-G.


Newly constructed FACIT subscales then undergo an initial assessment of reliability and validity using a sample of at least 50 patients. The validation design typically involves patient completion of a baseline assessment, a test-retest assessment 3–7 days later, and a third assessment 2–3 months later to demonstrate sensitivity to change over time. Relevant sociodemographic and treatment data is also collected and a battery of other measures administered at the baseline and 2–3 month retest to help determine convergent and divergent validity. A comprehensive analysis of the data gathered (including item response theory modeling when sample size allows) yields useful psychometric information and establishes initial reliability and validity of the scale.


Further details regarding the development and validation of specific FACIT measures can be found in the literature.

Interpretation

Higher scores for the scales and subscales indicate better quality of life. Average FACT-G scores for a group of patients can be compared to normative data to determine the HRQOL of the patients relative to the general U.S. population. These comparisons facilitate meaningful interpretation of HRQOL in patient populations. Though the body of literature is constantly evolving, normative data typically does not exist for disease-, symptom-, or condition-specific subscales.


FACIT measures have been shown to be responsive to change in both clinical and observational studies. Minimally important differences (MIDs) for scores of scales and subscales for some measures are available in the literature. An MID is the "smallest difference in score in the domain of interest that patients perceive as important, either beneficial or harmful, and that would lead the clinician to consider a change in the patient's management". MID estimates may vary across patients and possibly across patient groups; thus, ranges of MIDs have been identified for some scales, though it’s best to check the literature.
 

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Lauren Lent, DHA, MS
President, FACITtrans, LLC
Executive Director, FACIT.org
About Us
© 2020 FACIT.org 
Benjamin Arnold, MA
Executive Director, FACITtrans, LLC
Jason Bredle, MFA
Director, FACIT.org