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.
Further Reading
T. Pearman, B. Yanez, J. Peipert, K. Wortman, J. Beaumont, and D. Cella, "Ambulatory cancer and US general population reference values and cutoff scores for the functional assessment of cancer therapy," Cancer, vol. 120, no. 18, pp. 2902-2909, 2014.
P. S. Brucker, K. Yost, J. Cashy, K. Webster, and D. Cella, "General population and cancer patient norms for the Functional Assessment of Cancer Therapy-General (FACT-G)," Evaluation & the health professions, vol. 28, no. 2, pp. 192-211, 2005.
Z. Butt, J. Peipert, K. Webster, C. Chen, and D. Cella, "General population norms for the functional assessment of cancer therapy–Kidney Symptom Index (FKSI)," Cancer, vol. 119, no. 2, pp. 429-437, 2013.
B. Holzner et al., "Normative data for functional assessment of cancer therapy general scale and its use for the interpretation of quality of life scores in cancer survivors," Acta Oncologica, vol. 43, no. 2, pp. 153-160, 2004.
M. Janda, T. DiSipio, C. Hurst, D. Cella, and B. Newman, "The Queensland cancer risk study: general population norms for the Functional Assessment of Cancer Therapy–General (FACT‐G)," Psycho‐Oncology: Journal of the Psychological, Social and Behavioral Dimensions of Cancer, vol. 18, no. 6, pp. 606-614, 2009.
A.-S. L. Bagge, A. Carlander, C. Fahlke, and R. O. Bagge, "Health-Related Quality of Life (FACT-GP) in General Swedish Population," European Journal of Surgical Oncology, vol. 46, no. 2, pp. e7-e8, 2020.
I. Montan, B. Löwe, D. Cella, A. Mehnert, and A. Hinz, "General population norms for the functional assessment of chronic illness therapy (FACIT)-Fatigue Scale," Value in Health, vol. 21, no. 11, pp. 1313-1321, 2018.
D. Cella, J. s. Lai, C. H. Chang, A. Peterman, and M. Slavin, "Fatigue in cancer patients compared with fatigue in the general United States population," Cancer, vol. 94, no. 2, pp. 528-538, 2002.
D. Cella, M. J. Zagari, C. Vandoros, D. D. Gagnon, H.-J. Hurtz, and J. W. Nortier, "Epoetin alfa treatment results in clinically significant improvements in quality of life in anemic cancer patients when referenced to the general population," Journal of Clinical Oncology, vol. 21, no. 2.
M. Lange, N. Heutte, N. Morel, F. Eustache, F. Joly, and B. Giffard, "Cognitive complaints in cancer: The French version of the Functional Assessment of Cancer Therapy–Cognitive Function (FACT-Cog), normative data from a healthy population," Neuropsychological rehabilitation, vol. 26, no. 3, pp. 392-409, 2016.
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A. R. Munoz, J. M. Salsman, K. D. Stein, and D. Cella, "Reference values of the Functional Assessment of Chronic Illness Therapy‐Spiritual Well‐Being: A report from the American Cancer Society's studies of cancer survivors," Cancer, vol. 121, no. 11, pp. 1838-1844, 2015.
G. R. Norman, F. G. Sridhar, G. H. Guyatt, and S. D. Walter, "Relation of distribution-and anchor-based approaches in interpretation of changes in health-related quality of life," Medical care, pp. 1039-1047, 2001.
D. Cella, D. T. Eton, J.-S. Lai, A. H. Peterman, and D. E. Merkel, "Combining anchor and distribution-based methods to derive minimal clinically important differences on the Functional Assessment of Cancer Therapy (FACT) anemia and fatigue scales," Journal of pain and symptom management, vol. 24, no. 6, pp. 547-561, 2002.
R. R. Hay, D., "Reliability and validity (including responsiveness)," in Assessing Quality of Life in Clinical Trials: Methods and Practice, P. F. R. Hays Ed., 2nd ed. Oxford, NY: Oxford University Press, 2005, pp. 525-539.
T. Devji et al., "Evaluating the credibility of anchor based estimates of minimal important differences for patient reported outcomes: instrument development and reliability study," bmj, vol. 369, 2020.
K. J. Yost and D. T. Eton, "Combining distribution-and anchor-based approaches to determine minimally important differences: the FACIT experience," Evaluation & the health professions, vol. 28, no. 2, pp. 172-191, 2005.
D. Victorson, M. Soni, and D. Cella, "Metaanalysis of the correlation between radiographic tumor response and patient‐reported outcomes," Cancer: Interdisciplinary International Journal of the American Cancer Society, vol. 106, no. 3, pp. 494-504, 2006.
P. M. Fayers and R. D. Hays, "Don’t middle your MIDs: regression to the mean shrinks estimates of minimally important differences," Quality of Life Research, vol. 23, no. 1, pp. 1-4, 2014.
J. M. Salsman, J. L. Beaumont, K. Wortman, Y. Yan, J. Friend, and D. Cella, "Brief versions of the FACIT-fatigue and FAACT subscales for patients with non-small cell lung cancer cachexia," Supportive Care in Cancer, vol. 23, no. 5, pp. 1355-1364, 2015.
P. Rebelo, A. Oliveira, L. Andrade, C. Valente, and A. Marques, "Minimal Clinically Important Differences for Patient-Reported Outcome Measures of Fatigue in Patients With COPD Following Pulmonary Rehabilitation," Chest, vol. 158, no. 2, pp. 550-561, 2020.
S. N. Garland et al., "Prospective evaluation of the reliability, validity, and minimally important difference of the functional assessment of cancer therapy‐gastric (FACT‐Ga) quality‐of‐life instrument," Cancer, vol. 117, no. 6, pp. 1302-1312, 2011.
J. D. Peipert et al., "Validation of the Functional Assessment of Cancer Therapy–Leukemia instrument in patients with acute myeloid leukemia who are not candidates for intensive therapy," Cancer, vol. 126, no. 15, pp. 3542-3551, 2020.
M. T. King, M. Agar, D. C. Currow, J. Hardy, B. Fazekas, and N. McCaffrey, "Assessing quality of life in palliative care settings: head-to-head comparison of four patient-reported outcome measures (EORTC QLQ-C15-PAL, FACT-Pal, FACT-Pal-14, FACT-G7)," Supportive Care in Cancer, vol. 28, no. 1, pp. 141-153, 2020.
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D. Cella et al., "Validity of the FACT Hepatobiliary (FACT-Hep) questionnaire for assessing disease-related symptoms and health-related quality of life in patients with metastatic pancreatic cancer," Quality of Life Research, vol. 22, no. 5, pp. 1105-1112, 2013.
D. Cella et al., "What is a clinically meaningful change on the functional assessment of Cancer therapy–lung (FACT-L) questionnaire?: results from eastern cooperative oncology group (ECOG) study 5592," Journal of clinical epidemiology, vol. 55, no. 3, pp. 285-295, 2002.
D. Cella, M. B. Nichol, D. Eton, J. B. Nelson, and P. Mulani, "Estimating clinically meaningful changes for the Functional Assessment of Cancer Therapy—Prostate: results from a clinical trial of patients with metastatic hormone-refractory prostate cancer," Value in Health, vol. 12, no. 1, pp. 124-129, 2009.
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