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FACITtrans Publications OUR PASSION FOR LINGUISTICS IS WHAT DRIVES US Below are selected publications and presentations by our staff, detailing the various approaches and challenges to attaining cross-cultural measurement equivalence. PublicationsTop Your search returned 0 results Translatability Assessment of the Fluid Overload from Nephrotic Syndrome Measure Pérez, B., Carlozzi, N., Arnold, B., Parks-Vernizzi, E., Herzberg, T., Peipert, J.D., Creguer, T., Hurt, C., Lai, J., Raghunathan, T., Ganatra, S., Hashemi, S., Scherr, B., Zhai, Y., Salmon, E. Quality of Life Research (2024) 33:S1–S235 Presented at ISOQOL Cologne, October 2024. Translation and linguistic validation of 24 PROMIS item banks into French Ahmed S, Parks-Vernizzi E, Perez B, Arnold B, Boucher A, Hossenbaccus M, Correia H, Bartlett SJ. Qual Life Res. 2024 Aug;33(8):2119-2127 . doi: 10.1007/s11136-024-03690-4. Epub 2024 Jun 12. PMID: 38865068; PMCID: PMC11286690. Dealing with the Complexities of Concurrently Translating and Implementing Electronic Clinical Outcome Assessments: Best Practices Roundtable: The Two-Step Dance of Concurrent Translation and Implementation of eCOAs Shalhoub, H., Turner, M., Bradley-Gilbride, A., Eremenco, S., Muhlan-Rehmer, H., Parks-Vernizzi, E., Arnold, B., Kulis, D., Anfray, C., Chaplin, J. Quality of Life Research (2024) 33:S1–S235 Presented at ISOQOL Cologne, October 2024. Translation and Linguistic Validation of the EXACT for Use with COPD Patients in African Countries Savic L., Arnold B., Baily A., Mackey DJ., Olaiya P. Presented at ISOQOL Calgary, October 2023. Winner of the 2023 Outstanding Poster Award Psychometric properties of the Polish version of the FACIT-Sp-12: Assessing spiritual well-being among patients with chronic diseases Machul M., Bredle J., Jurek K., Dobrowolska B. Med Sci Monit. 2023 Nov 16;29:e941769 Achieving Conceptual and Cultural Equivalence in Universal Arabic PROMIS Item Banks Parks-Vernizzi E., Nagy E., Guillen R., Boucher A., Arnold B., Maksud E., Correia H., Bakhsh HR, Bin Sheeha BH, Al-Dhahi M., Al-Hasani R. Presented at ISOQOL Calgary, October 2023. Translation & Linguistic Validation of the FACIT-COST Measuring Financial Toxicity in Cancer Patients Worldwide Savic L., Parks-Vernizzi E., Arnold B., Bredle JM Presented at ISOQOL Calgary, October 2023. Translation & Linguistic Validation of PROMIS SF v2.0 -Physical Function -MS 15a & PROMIS SF v1.0 -Fatigue -MS 8a for use in India & Malaysia Parks-Vernizzi E., Pérez B., Maksud E., Son J., Arnold B., Correia C. Presented at PHO Prague, October 2022. The Functional Assessment of Chronic Illness Therapy (FACIT) Measurement System: Guidance for Use in Research and Clinical Practice Webster, K.A., Peipert, J.D., Lent, L.F., Bredle, J., Cella, D. Kassianos, A.P. (eds) Handbook of Quality of Life in Cancer . Springer, Cham. https://doi.org/10.1007/978-3-030-84702-9_6 Pediatric and parent proxy Peds-FACT-Br translations and cross-cultural validation: challenges and resolutions Arnold B., Bredle J., Parks-Vernizzi E., Savic L. Presented at ISOQOL Prague, October 2022. Prev 1 2 3 ... 11 1 ... 1 2 3 4 5 6 7 8 9 10 11 ... 11 Next We look forward to serving your study needs. Contact us to get started! ​ FACITtrans is ISO 9001:2015 and ISO 17100:2015 certified.

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 The majority of 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. Reference Values Reference values are population values of a PRO instrument which can be a particular disease population or the general population. They are also often useful if generated for a particular political or geographical designation, e.g., at the country level. Such values can be useful for putting scores of an individual or group into context. Typically, reference values include averages, dispersion (e.g., standard deviation), ranges, or other aspects of the scores’ distributions. They are often reported for an overall sample and for key demographic groups (e.g., by age and sex). Reference values are most useful if they are estimated using a representative sample of patients, regardless of whether that is for the general population or a particular disease sample. Reference values can be applied usefully in both research and clinical settings. There have been multiple reports of reference values for FACIT instruments. In addition to the FACT-G, reference values have been published for the FACT-General Population (FACT-GP; general population sample); FACT Kidney Symptom Index instruments (FKSI; general population sample); FACIT-Fatigue (general population sample); FACT-Cognitive Function (FACT-Cog; healthy population); and the FACIT-Spiritual Wellbeing Scale (FACIT-Sp-12). We recommend that these reference values be used for comparison to scores from future research. Clinical and Other Anchors Anchor variables are very useful tools to help interpret FACIT score differences and change. Anchors are external criterion variables on which the magnitude of change on the construct of interest is well-understood and therefore can be used to “anchor” an interpretation of difference or change on the PRO of interest. Anchors are useful for multiple important applications in PRO-based research. First, anchors are used to test known-groups validity and responsiveness to change in the process of establishing a PRO’s psychometric properties. Second, and more germane to the interpretation of FACIT measures, there is now general consensus that anchor-based approaches are most appropriate for establishing thresholds for important differences and important changes at the group level. In this case, “differences” refer to cross-sectional, between-groups comparisons, and “changes” refer to within-group comparisons over time. Finally, anchoring PROs to clinically familiar differences and changes can help translate their meaning to patients and clinicians. Multiple types of anchors are useful for establishing important differences and changes. There is significant focus on patient reported anchors. Patient-reported anchors have the advantage of utilizing the same assessment method, and they typically assess changes that are meaningful to patients. In addition, when the patient-reported anchor represents the same construct as the PRO, we have more confidence that the difference or change estimates derived from an analysis using the anchor are relevant to the PRO. However, other types of anchors may be useful as well, especially in cancer research. For example, clinical variables that are not the same construct as the PRO but have a demonstrable relationship with the PRO, such as adverse events, tumor response, or progression, may be useful as well. However, any anchor used should be sufficiently correlated with the PRO to justify its use. We require a minimum correlation of 0.30 to justify use of an anchor; although correlations above 0.40 are preferred, as we have noted a paradox by which anchors with lower correlations tend to produce smaller estimates of important difference or change. Because this is essentially an exercise in acquiring multiple converging points of evidence, we advise use of multiple anchors that include patient report, clinician report, and objective clinical metrics (e.g., laboratory values; radiographic data). Important Differences and Change At the group level, determining the level of difference that is considered important to patients or other stakeholders over and above statistical significance can enhance interpretation because, with large sample sizes, even trivial differences can be statistically significant. Important difference estimates can be used to determine whether patient groups differ in HRQoL, and may be especially useful for planning future studies by providing a basis for power analyses. Similarly, important change estimates can indicate the amount of change that patients find meaningful or that indicate clinically important improvements or decrements. A previous summary of important differences and changes on FACIT instruments found relative consistency in the magnitude important differences in terms of proportion of the total scale points. In summary, the following ranges for important differences were found: FACT-G Total: 4–7% of total scores (3–7 units), FACT-G subscales: 7–11% (2–3 units), symptom-targeted instrument totals (e.g., Total FACT-Anemia, Total FACT-Breast, Total FACT-Colorectal, Total FACT-Head and Neck): 4–8% (5–12 units), and trial outcome indexes (e.g., Fatigue, Anemia, Biological Response Modifiers, Breast, Colorectal, Lung): 5–7% (4–7 units). This was a thorough aggregation of data up to 2005, but many studies estimating important differences for FACIT instruments, especially newer instruments or for non-cancer populations, have been published since that time. After collecting 15 additional years of data, these 2005 estimates have held true. We recommend that researchers consult the literature for up-to-date and appropriate important difference or change estimates for any given FACT or FACIT scale of interest. To implement this recommendation, it is important to use estimates of important change that have come from longitudinal studies actually focusing on change over time in the FACT or FACIT scale of interest, instead of substituting a cross-sectional estimate of the important difference where an estimate of important change is needed. There are a few reasons to distinguish between change versus difference estimates. First, analyses to estimate important change typically use change scores (i.e., difference between baseline and a post-baseline follow-up), which may be distributed differently than FACT/FACIT scale scores at a single cross-sectional cut. Second, the analyses used to determine change often differ from analyses to estimate important differences in some ways. Identifying important changes in terms of meaningfulness to patients is required to support the use of FACT/FACIT instruments in regulatory applications. The FDA, for one, has prioritized estimating meaningful change thresholds for PROs using patient-reported anchors that measure the same construct or domain of the PRO to be used as an endpoint in trials to show treatment benefit. A very common anchor for this kind of application is the patient global impression of change (PGIC), which retrospectively asks the patients how much they have changed on a domain of interest over a clinically relevant period of time and a set of discreet response options to characterize this change. Then, the difference in mean PRO change scores can be examined over the PGIC response options to determine the amount of change on the PRO associated with meaningful categories as defined on the PGIC, e.g., difference in mean PRO change scores between patients reporting being “about the same” and “a little worse” on the PGIC anchor. To help interpret these differences, empirical cumulative distribution plots (eCDF) can be created and plotted to represent change on the PRO within each anchor category. A useful alternative to the PGIC may be to examine prospective change in a similar item, the patient global impression of severity (PGIS), which assesses the level of symptom severity at a given time point. Responder Definition An important step in interpreting a PRO is to identify the responder definition, or the amount of change at the individual level that should be interpreted as treatment benefit. Used alone, group-level estimates of change on PROs may not be appropriate for classifying individuals as having changed. Identifying responders to treatment requires determining whether the change for an individual patient is significant, and group-level estimates of change (e.g., from important difference or change analyses) may under-estimate this. This view is in contrast to current regulatory focus on defining responders in terms of meaningful change based on a patient-reported anchor; such methods are necessarily group-based, focusing on identifying the average change for the group of individuals who said they changed on an anchor. In contrast to this approach, other authors have argued that, “a minimum standard for saying an individual has responded (improved) should include that the change in score is statistically significant.” Since it often requires large changes, statistically significant change at the individual level may also be meaningful to the individual. 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. For more information about any of the above, please refer to: Webster, K.A., Peipert, J.D., Lent, L.F., Bredle, J., Cella, D. (2022). The Functional Assessment of Chronic Illness Therapy (FACIT) Measurement System: Guidance for Use in Research and Clinical Practice. In: Kassianos, A.P. (eds) Handbook of Quality of Life in Cancer. Springer, Cham. https://doi.org/10.1007/978-3-030-84702-9_6 Further Reading Webster, K.A., Peipert, J.D., Lent, L.F., Bredle, J., Cella, D. (2022). The Functional Assessment of Chronic Illness Therapy (FACIT) Measurement System: Guidance for Use in Research and Clinical Practice. In: Kassianos, A.P. (eds) Handbook of Quality of Life in Cancer. Springer, Cham. https://doi.org/10.1007/978-3-030-84702-9_6 ​ 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. J.-S. Lai et al., "Parent-perceived child cognitive function: results from a sample drawn from the US general population," Child's Nervous System, vol. 27, no. 2, pp. 285-293, 2011. 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. S. Yount et al., "A randomized validation study comparing embedded versus extracted FACT Head and Neck Symptom Index scores," Quality of Life Research, vol. 16, no. 10, pp. 1615-1626, 2007. 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. ​ J. Steel, D. T. Eton, D. Cella, M. Olek, and B. Carr, "Clinically meaningful changes in health-related quality of life in patients diagnosed with hepatobiliary carcinoma," Annals of Oncology, vol. 17, no. 2, pp. 304-312, 2006. ​ R. Jaeschke, J. Singer, and G. H. Guyatt, "Measurement of health status. Ascertaining the minimal clinically important difference," (in eng), Control Clin Trials, vol. 10, no. 4, pp. 407-15, Dec 1989, doi: 10.1016/0197-2456(89)90005-6. H. L. Cheng et al., "Psychometric testing of the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group—Neurotoxicity (FACT/GOG-Ntx) subscale in a longitudinal study of cancer patients treated with chemotherapy," Health and quality of life outcomes, vol. 18, no. 1, pp. 1-9, 2020. S.-F. Wong et al., "A prospective study to validate the functional assessment of cancer therapy (FACT) for epidermal growth factor receptor inhibitor (EGFRI)-induced dermatologic toxicities FACT-EGFRI 18 questionnaire: SWOG S1013," Journal of patient-reported outcomes, vol. 4, no. 1, pp. 1-12, 2020. ​ U. F. a. D. Administration, "Discussion Document for Patient-Focused Drug Development Public Workshop on Guidance 4: Incorporating Clinical Outcome Assessments into Endpoints for Regulatory Decision-Making," United States Department of Health and Human Services, Silver Spring, MD, 2019. U. F. a. D. Administration, "Discussion Document for Patient-Focused Drug Development Public Workshop on Guidance 3: Select, Develop or Modify Fit-for-Purpose Clinical Outcome Assessments," United States Department of Health and Human Services, Silver Spring, MD, 2018. ​ R. E. Jensen et al., "Validation of the PROMIS physical function measures in a diverse US population-based cohort of cancer patients," Quality of life research, vol. 24, no. 10, pp. 2333-2344, 2015. R. E. Jensen et al., "Responsiveness of 8 Patient‐Reported Outcomes Measurement Information System (PROMIS) measures in a large, community‐based cancer study cohort," Cancer, vol. 123, no. 2, pp. 327-335, 2017. C. D. Coon and K. F. Cook, "Moving from significance to real-world meaning: methods for interpreting change in clinical outcome assessment scores," Quality of Life Research, vol. 27, no. 1, pp. 33-40, 2018. H. R. D. P. J. D, "Minimally Important Differences Do Not Identify Responders to Treatment," JOJ Sciences, Juniper Publishers Inc., vol. 1, no. 1, pp. 4-5, 2018. G. R. Norman, P. Stratford, and G. Regehr, "Methodological problems in the retrospective computation of responsiveness to change: the lesson of Cronbach," Journal of clinical epidemiology, vol. 50, no. 8, pp. 869-879, 1997. L. D. McLeod, C. D. Coon, S. A. Martin, S. E. Fehnel, and R. D. Hays, "Interpreting patient-reported outcome results: US FDA guidance and emerging methods," Expert review of pharmacoeconomics & outcomes research, vol. 11, no. 2, pp. 163-169, 2011. R. D. Hays, M. Brodsky, M. F. Johnston, K. L. Spritzer, and K.-K. Hui, "Evaluating the statistical significance of health-related quality-of-life change in individual patients," Evaluation & the Health Professions, vol. 28, no. 2, pp. 160-171, 2005. M. T. King, A. C. Dueck, and D. A. Revicki, "Can methods developed for interpreting group-level patient-reported outcome data be applied to individual patient management?," Medical care, vol. 57, no. Suppl 5 1, p. S38, 2019. N. S. Jacobson and P. Truax, "Clinical significance: A statistical approach to defining meaningful change in psychotherapy research," Journal of Consulting and Clinical Psychology, vol. 59, no. 1, pp. 12-19, 1991, doi: 10.1037/0022-006X.59.1.12. R. D. Hays, K. L. Spritzer, C. D. 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FACIT Translation & Linguistic Validation Methodology First published in 1996 and adopted by the HealthMeasures family of measurement systems (Patient-Reported Outcomes Measurement Information System (PROMIS®); Quality of Life in Neurological Disorders (Neuro-QoL); NIH Toolbox; as well as the Critical Path Institute's PRO Consortium; the FACIT Translation and Linguistic validation methodology emphasizes a “universal” translation approach in order to achieve a single, valid translation for each language, designed to work across different countries that speak the same language. The universal approach provides several advantages to a country-specific approach that produces multiple same-language versions across different countries. These advantages include the following: (1) enables language subgroup comparison, without requiring a check on bias introduced by different translations (e.g., comparing Spanish-speaking groups in the United States to one another or to people in Spain or Latin America); (2) minimizes bias introduced by multiple, country-specific translations in a project or trial; (3) simplifies logistics and analysis of multinational clinical trials; and (4) facilitates survey administration in the case of migrating populations. In cases requiring a universal translation, the standard methodology is modified during the translation and review steps and in cognitive debriefing to include native linguists from each relevant country. This is a more rigorous version of the double-back-translation method considered superior to single translation and translation by committee (Bonomi et al., 1996). The process is summarized as follows: For each target language, the source is translated by two independent professional translators. Next, a third independent translator reconciles the two forward translations by choosing the better of the two forward translations and resolving discrepancies between them. This reconciled version is then back-translated blindly by a native English-speaking translator fluent in the target language. The developer reviews the back-translation for discrepancies from the source version and to assess equivalence with the source. Subsequently, an additional independent review/finalization is performed by a native speaking linguist and harmonization with other existing translations is conducted by the developer in conjunction with the linguist. Finally, the target-language version is pretested with patients in the country in which the language is spoken. If any items are found to be problematic by patients, their feedback allows for modifications in the translations and for indications of changes that may later be made to the original source document, an example of the decentering process (Lent, Hahn, Eremenco, Webster, & Cella, 1999). The Universal Approach: For languages native to multiple countries, representatives from each country work together in conjunction with FACIT to develop one version of an item valid for use in every country a language is spoken, rather than country-specific versions. Learn more about FACITtrans’ services All items in the FACIT Measurement System undergo this methodology and FACITtrans is the only vendor authorized to translate and linguistically validate FACIT items. Permission to translate any FACIT item or measure should be obtained prior to undertaking this methodology. Contact us for more details. If permission has not been obtained, the item or scale will not be recognized as part of the FACIT measurement system.

Scoring OF THE FACIT MEASURES For all FACIT measures, higher scores are better than lower scores. This is true whether measuring a symptom or a functional ability. All FACIT measures use raw total scoring approach without subsequent transformation. Scoring recommendations permit for a variety of component and composite calculations, depending on the desired outcome assessment, meeting FDA guidance recommendations for both global and targeted symptom evaluation. For any FACIT measure, subscale scores are calculated by first reversing negatively stated-items (subtracting the response from ‘4’) and then summing the raw (0-4) scores. A total score is then derived by summing subscale scores. For example, a total FACT-G score is obtained by summing individual subscale scores PWB + EWB + SWB + FWB. Total scores for the disease-, treatment-, and condition-specific subscales are typically obtained by summing all subscale scores PWB + EWB + SWB + FWB + additional concerns subscale. The scoring templates provided for each measure simplify this process by providing a framework to reverse score relevant items and prorate for missing data. TOI The TOI can be computed for any FACIT disease-, treatment-, or condition-specific scale. It is the sum of the Physical Well-Being (PWB), Functional Well-Being (FWB), and additional concerns subscales. Our experience with this TOI endpoint is that it is an efficient summary index of physical/functional outcomes. It is therefore a common endpoint used in clinical trials, because it is responsive to change in physical/functional outcomes, sometimes more than a total (overall) multidimensional aggregated score, which includes social and emotional well-being. While social and emotional well-being are very important to quality of life, they are not as likely to change as quickly or dramatically over time or in response to physical health interventions such as pharmaceutical treatments in clinical trials. Missing Data Relevant scoring options are outlined on each measure’s scoring template, where calculating reverse scored items and prorating for missing data has been integrated. In cases where individual items are skipped, subscale scores can be prorated using the average of the other answers in the scale. This is acceptable as long as more than 50% of the items were answered in the subscale (e.g., a minimum of 4 of 7 items, 4 of 6 items, etc.). The total score is then calculated as the sum of the un-weighted subscale scores. A FACIT measure is considered to be an acceptable indicator of patient quality of life as long as overall item response rate is greater than 80% (e.g., at least 22 of 27 FACT-G items completed). This is not to be confused with individual subscale item response rate, which allows a subscale score to be prorated for missing items if greater than 50% of items are answered. In addition, a total score should only be calculated if ALL of the component subscales have valid scores. Scoring is intended to be completed by research and clinical staff rather than patients themselves. Raw scoring templates are available in English and electronic scoring options are currently not available from FACIT.

CELEBRATING 15 YEARS as a boutique language service provider LEARN MORE Welcome to FACIT Welcome to FACIT FACITtrans provides clinical outcomes assessment (COA) translatability assessment, questionnaire, consent form and protocol translation, interview transcription and translation, eCOA adaptation and migration, linguistic validation and more. FACIT.org manages the distribution of and information related to more than 100 questionnaires that measure health-related quality of life for people with chronic illnesses. Together we are pioneers in the field of patient-centered research with 25+ years of cutting-edge science and services. A 不 Translation & Linguistic Validation Services FACIT Measurement System FACIT Searchable Library COA Management & Licensing Services NEWS We are happy to announce our publication in Quality of Life Research : Translation and linguistic validation of 24 PROMIS item banks into French Need help? Contact us.

FACIT Measures & Searchable Library OVERVIEW The FACIT Measurement System is a collection of over 700 items, 130 pediatric items, and 100 validated measures targeted to the management of chronic illness. "FACIT" (Functional Assessment of Chronic Illness Therapy) was adopted as the formal name of the measurement system in 1997 to portray the expansion of the "FACT" (Functional Assessment of Cancer Therapy) measures into other chronic illnesses and conditions. The measurement system, under development since 1988, is a comprehensive collection of patient reported measures that assess general health-related quality of life (HRQoL) and specific disease- and treatment-related concerns across multiple chronic illnesses and the general population. The measurement system (originally referred to as the Functional Assessment of Cancer Therapy, or FACT) emerged from a conceptual framework for quality of life in the context of health status that is centered on two essential principles: subjectivity and multidimensionality. HRQoL is uniquely personal, defined by patient experiences and influenced by one’s subjective perspective. Therefore, HRQoL is best assessed by direct-report. HRQoL is multidimensional, including, but not limited to symptoms, side effects, and functional status. It also includes more general appraisals of life quality and value. Meaningful assessment comes from asking patients about these distinct, yet often correlated areas of function and well-being. There is general consensus that key domains of HRQoL include physical, functional, emotional, and social/family well-being. Physical well-being refers to perceived and observed bodily function or disruption and includes symptoms such as pain, fatigue, and nausea. Functional well-being refers to one’s ability to perform the activities related to one’s personal needs, ambitions, or social role and includes things like ability to work, sleep, and enjoy life. Emotional well-being covers positive and negative affect as well as life enjoyment and appreciation. Social/family well-being includes a broader range of perceived support, leisure activities, family wellbeing, and intimacy. Over time, this framework has expanded to include additional targeted domains such as disease-specific symptoms and treatment side effects for more comprehensive and clinically relevant assessment Validation of the core measure allowed for the evolution of multiple disease, treatment, condition, and other targeted measures. FACIT scales are constructed to complement the FACT-G, addressing relevant disease-, treatment-, or condition-related issues not already covered in the general questionnaire. Each is intended to be as specific as necessary to capture the clinically-relevant problems associated with a given condition or symptom, yet general enough to allow for comparison across diseases, and extension, as appropriate, to other chronic medical conditions. The FACIT Measurement System now includes over 700 items, some of which have been translated into more than 80 languages. Assessment of any one patient is tailored so that the most-relevant questions are asked and administration time for any one assessment is usually less than 15 minutes. The majority of FACIT items have demonstrated face and content validity and were created with direct input from patients and expert clinicians. More recently, FACIT has expanded its catalog of items to the FACIT Searchable Library , where one can create a custom form using the site’s Build-a-PRO function and include only those FACIT items most relevant to one’s study or purpose. While doing so does not instantly validate the custom composition, it does create an opportunity to select specific items relevant to the research question at hand, using content valid items that have undergone careful translation into other languages. It also allows for the opportunity to pursue validation of the assembled set of questions using standard questionnaire validation practice. The practice of selecting an established, fixed FACIT measure is still recommended for any investigator or clinician wishing to obtain a valid, interpretable score on the endpoints provided by that FACIT measure. FACIT Measures FACIT Searchable Library Administration Scoring eCOA Guidance Interpretation Translation & Linguistic Validation Methodology Label Claims FACIT Utilities

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