Hostname: page-component-745bb68f8f-b6zl4 Total loading time: 0 Render date: 2025-02-11T07:01:30.118Z Has data issue: false hasContentIssue false
  • English
  • Français

Linking Scores with Patient-Reported Health Outcome Instruments: A Validation Study and Comparison of Three Linking Methods

Published online by Cambridge University Press:  01 January 2025

Benjamin D. Schalet Open the ORCID record for Benjamin D. Schalet [Opens in a new window] ,
Sangdon Lim Open the ORCID record for Sangdon Lim [Opens in a new window] ,
David Cella  and
Seung W. Choi Open the ORCID record for Seung W. Choi [Opens in a new window]
Benjamin D. Schalet*
Affiliation:
Northwestern University, Feinberg School of Medicine
Sangdon Lim
Affiliation:
The University of Texas at Austin
David Cella
Affiliation:
Northwestern University, Feinberg School of Medicine
Seung W. Choi
Affiliation:
The University of Texas at Austin
*
Correspondence should be made to Benjamin D. Schalet, Department of Medical Social Sciences, North–western University, Feinberg School of Medicine, 625 N Michigan Ave, 21st Floor, Chicago, IL60611, USA. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The psychometric process used to establish a relationship between the scores of two (or more) instruments is generically referred to as linking. When two instruments with the same content and statistical test specifications are linked, these instruments are said to be equated. Linking and equating procedures have long been used for practical benefit in educational testing. In recent years, health outcome researchers have increasingly applied linking techniques to patient-reported outcome (PRO) data. However, these applications have some noteworthy purposes and associated methodological questions. Purposes for linking health outcomes include the harmonization of data across studies or settings (enabling increased power in hypothesis testing), the aggregation of summed score data by means of score crosswalk tables, and score conversion in clinical settings where new instruments are introduced, but an interpretable connection to historical data is needed. When two PRO instruments are linked, assumptions for equating are typically not met and the extent to which those assumptions are violated becomes a decision point around how (and whether) to proceed with linking. We demonstrate multiple linking procedures—equipercentile, unidimensional IRT calibration, and calibrated projection—with the Patient-Reported Outcomes Measurement Information System Depression bank and the Patient Health Questionnaire-9. We validate this link across two samples and simulate different instrument correlation levels to provide guidance around which linking method is preferred. Finally, we discuss some remaining issues and directions for psychometric research in linking PRO instruments.

Keywords

patient-reported outcomeslinkingscale alignmentdepressionPROMISPHQ-9calibrated projection
Type
Application Reviews and Case Studies
Information
Psychometrika , Volume 86 , Issue 3: Special Section: Advancing Methods to Assess Patient-Reported Outcomes: Lessons Learned from the Patient-Reported Outcomes Measurement Information System® (PROMIS®) Initiative , September 2021 , pp. 717 - 746
Copyright
Copyright © 2021 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahmed, S., Berzon, R. A. Revicki, D. A. Lenderking, W. R. Moinpour, C. M., Basch, E., Reeve, B. B. Wu, A. W. & International Society for Quality of Life Research (2012). The use of patient-reported outcomes (PRO) within comparative effectiveness research: implications for clinical practice and health care policy. Medical Care, 50(12), 1060–1070.CrossRefGoogle Scholar
Albano, A. D. equate: An R package for observed-score linking and equating.Journal of Statistical Software,(2016).74(8)1–36CrossRefGoogle Scholar
Amtmann, D,Cook, K. F., Jensen, M.P., Chen, W-H, Choi, S,Revicki, D,Callahan, L Development of a PROMIS item bank to measure pain interference. Pain,(2010).150(1)173–182 20554116 2916053CrossRefGoogle ScholarPubMed
Angoff, W. H. (1971). Scales norm., and equivalent scores. In R.L. Thorndike (Ed.) Educational measurement. (2nd ed., pp. 508–600). Washington, DC: American Council on Education.Google Scholar
Askew, R. L.,Kim, J,Chung, H,Cook, K. F.,Johnson, K. L.,Amtmann, DDevelopment of a crosswalk for pain interference measured by the BPI and PROMIS pain interference short form.Quality of Life Research,(2013).22(10)2769–2776CrossRefGoogle ScholarPubMed
Basch, ENew frontiers in patient-reported outcomes: Adverse event reporting, comparative effectiveness, and quality assessment.Annual Review of Medicine,(2014).65,307–317CrossRefGoogle ScholarPubMed
Basch, E,Spertus, J,Dudley, R. A.,Wu, A,Chuahan, C,Cohen, P,Christensen, KMethods for developing patient-reported outcome-based performance measures (PRO-PMs).Value in Health,(2015).18(4)493–504CrossRefGoogle ScholarPubMed
Baumhauer, J. F.,Bozic, K. J.Value-based healthcare: Patient-reported outcomes in clinical decision making.Clinical Orthopaedics and Related Research®,(2016).474(6)1375–1378CrossRefGoogle ScholarPubMed
Bland, J. M.,Altman, D. G.Measuring agreement in method comparison studies.Statistical Methods in Medical Research,(1999).8(2)135–160CrossRefGoogle ScholarPubMed
Bock, R. D., Mislevy, R. J.Adaptive EAP estimation of ability in a microcomputer environment.Applied Psychological Measurement,(1982).6(4)431–444CrossRefGoogle Scholar
Brennan, R. (2004). Linking with Equivalent Group or Single Group Design (LEGS; Version 2.0)[Computer software]. Iowa City, IA: University of Iowa, Center for Advanced Studies in Measurement and Assessment (CASMA).Google Scholar
Browne, M. W., Cudeck, RAlternative ways of assessing model fit.Sociological Methods and Research,(1992).21(2)230–258CrossRefGoogle Scholar
Bryant, D. U. Smith, A. K. Alexander, S. G. Vaughn, K., & Canali, K. G. (2005). Expected a posteriori estimation of multiple latent traits (518612013-445)CrossRefGoogle Scholar
Buysse, D. J., Yu, L, Moul, D. E., Germain, A, Stover, A, Dodds, N. E., Pilkonis, P. A.Development and validation of patient-reported outcome measures for sleep disturbance and sleep-related impairments.Sleep,(2010).33(6)781–79220550019 2880437CrossRefGoogle ScholarPubMed
Cai, LLord–Wingersky algorithm version 2.0 for hierarchical item factor models with applications in test scoring, scale alignment, and model fit testing.Psychometrika,(2015).80(2)535–559CrossRefGoogle ScholarPubMed
Carstensen, BComparing methods of measurement: Extending the LoA by regression.Statistics in Medicine,(2010).29(3)401–410CrossRefGoogle ScholarPubMed
Cella, D, Choi, S. W., Condon, D. M., Schalet, B, Hays, R. D., Rothrock, N. E., Amtmann, DPROMIS® adult health profiles: Efficient short-form measures of seven health domains.Value in Health,(2019).22(5)537–5447201383CrossRefGoogle ScholarPubMed
Cella, D, Riley, W, Stone, A, Rothrock, N, Reeve, B, Yount, S, Choi, SThe patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008.Journal of Clinical Epidemiology,(2010).63(11)1179–11942965562CrossRefGoogle ScholarPubMed
Cella, D., Schalet, B. Kallen, M., Lai, J.-S., Cook, K., Rutsohn, J., & Choi, S. (2016). PROSETTA stone analysis report: A rosetta stone for patient reported outcomes.Google Scholar
Cella, D, Stone, A. A.Health-related quality of life measurement in oncology: Advances and opportunities.American Psychologist,(2015).70(2)175CrossRefGoogle ScholarPubMed
Cella, D, Yount, S, Rothrock, N, Gershon, R, Cook, K, Reeve, B, Rose, MThe patient-reported outcomes measurement information system (PROMIS): Progress of an NIH Roadmap cooperative group during its first two years.Medical Care,(2007).45(5 Suppl 1S32829758CrossRefGoogle ScholarPubMed
Chalmers, R. P. mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48 (6), 1–29 (2012).CrossRefGoogle Scholar
Choi, S, Lim, S, Schalet, B, Kaat, A, & Cella, D. (2020). PROsetta: Linking Patient-Reported Outcomes Measures. R package version 0.2.0, https://cran.r-project.org/package=PROsettaGoogle Scholar
Choi, S. W., Gibbons, L. E., Crane, P. K.Lordif: An R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations.Journal of Statistical Software,(2011).39(8)13093114CrossRefGoogle Scholar
Choi, S. W., Schalet, B, Cook, K. F., Cella, DEstablishing a common metric for depressive symptoms: Linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression.Psychological Assessment,(2014).26 25135515387CrossRefGoogle ScholarPubMed
Cleeland, C. S., Gonin, R, Hatfield, A. K., Edmonson, J. H., Blum, R. H., Stewart, J. A., Pandya, K. J.Pain and its treatment in outpatients with metastatic cancer.New England Journal of Medicine,(1994).330 9592–596CrossRefGoogle ScholarPubMed
Cook, K. F., Schalet, B. D., Kallen, M. A., Rutsohn, J. P., Cella, DEstablishing a common metric for self-reported pain: Linking BPI pain interference and SF-36 bodily pain subscale scores to the PROMIS pain interference metric.Quality of Life Research,(2015).24 102305–23184567433CrossRefGoogle Scholar
Coster, W. J., Ni, P, Slavin, M. D., Kisala, P. A., Nandakumar, R, Mulcahey, M. J., Jette, A. M.Differential item functioning in the patient reported outcomes measurement information system pediatric short forms in a sample of children and adolescents with cerebral palsy.Developmental Medicine and Child Neurology,(2016).58 111132–11385052096CrossRefGoogle Scholar
Curran, P.J., Hussong, A.M. Integrative data analysis: The simultaneous analysis of multiple data sets.Psychological Methods,(2009).14 281–100CrossRefGoogle ScholarPubMed
De Vet, H. C., Terwee, C. B., Mokkink, L. B., Knol, D. L.Measurement in medicine: A practical guide,(2011).Cambridge:Cambridge University PressCrossRefGoogle Scholar
Dorans, N. J. Equating concordanc. and expectation. Applied Psychological Measurement,(2004).28 4227–246CrossRefGoogle Scholar
Dorans, N. J.Linking scores from multiple health outcome instruments.Quality of Life Research,(2007).16 185–94CrossRefGoogle ScholarPubMed
Dorans, N. J., Holland, P. W.Population invariance and the equatability of tests: Basic theory and the linear case.ETS Research Report Series,(2000).2000 2i–35CrossRefGoogle Scholar
Dorans, N. J., Lyu, C. F., Pommerich, M, Houston, W. M.Concordance between ACT assessment and recentered SAT I sum scores.College and University,(1997).73 224–32Google Scholar
Fischer, H. F., Rose, MScoring depression on a common metric: A comparison of EAP estimation, plausible value imputation, and full Bayesian IRT modeling.Multivariate Behavioral Research,(2019).54 185–99CrossRefGoogle ScholarPubMed
Fischer, H. F., Wahl, I, Fliege, H, Klapp, B. F., Rose, MImpact of cross-calibration methods on the interpretation of a treatment comparison study using 2 depression scales.Medical Care,(2012).50 4320–326CrossRefGoogle ScholarPubMed
Gershon, R. C., Lai, J. S., Bode, R, Choi, S, Moy, C, Bleck, T, Cella, DNeuro-QOL: Quality of life item banks for adults with neurological disorders: item development and calibrations based upon clinical and general population testing.Quality of Life Research,(2012).21 3475–486CrossRefGoogle ScholarPubMed
Gottfredson, N. C., Cole, V. T., Giordano, M. L., Bauer, D. J., Hussong, A. M., Ennett, S. T.Simplifying the implementation of modern scale scoring methods with an automated R package: Automated moderated nonlinear factor analysis (aMNLFA).Addictive Behaviors,(2019).94, 65–73CrossRefGoogle ScholarPubMed
Haebara, TEquating logistic ability scales by a weighted least squares method.Japanese Psychological Research,(1980).22 3144–149CrossRefGoogle Scholar
Hahn, E. A., DeWalt, D. A., Bode, R. K., Garcia, S. F., DeVellis, R. F., Correia, H, Cella, DNew English and Spanish social health measures will facilitate evaluating health determinants.Health Psychology,(2014).33 54904159098CrossRefGoogle ScholarPubMed
Hansen, M, Cai, L, Stucky, B. D., Tucker, J. S., Shadel, W. G., Edelen, M. O.Methodology for developing and evaluating the PROMIS® smoking item banks.Nicotine and Tobacco Research,(2014).16 Suppl 3S175–S189CrossRefGoogle ScholarPubMed
Hanson, B. A., Zeng, L, Colton, D. A.A comparison of presmoothing and postsmoothing methods in equipercentile equating,(1994).New York:American College Testing ProgramGoogle Scholar
Hays, R. D., Brodsky, M, Johnston, M. F., Spritzer, K. L., Hui, K. -K.Evaluating the statistical significance of health-related quality-of-life change in individual patients.Evaluation and the Health Professions,(2005).28 2160–171CrossRefGoogle ScholarPubMed
Hays, R. D., Liu, H, Kapteyn, AUse of Internet panels to conduct surveys.Behavior Research Methods,(2015).47 3685–6904546874CrossRefGoogle ScholarPubMed
Holland, P. W., Dorans, N. J.Linking and equating.Educational Measurement,(2006).4, 187–220Google Scholar
Hu, L-T, Bentler, P. M.Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification.Psychological Methods,(1998).3 4424CrossRefGoogle Scholar
Hu, L, Bentler, P. M.Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives.Structural Equation Modeling: A Multidisciplinary Journal,(1999).6 11–55CrossRefGoogle Scholar
Hussong, A. M., Gottfredson, N. C., Bauer, D. J., Curran, P. J., Haroon, M, Chandler, R, Springer, S. A.Approaches for creating comparable measures of alcohol use symptoms: Harmonization with eight studies of criminal justice populations.Drug and Alcohol Dependence,(2019).194, 59–68CrossRefGoogle ScholarPubMed
Jensen, R. E. Moinpour, C. M. Potosky, A. L. Lobo, T. Hahn, E. A. Hays, R. D. et al. (2017). Responsiveness of 8 Patient-Reported Outcomes Measurement Information System (PROMIS) measures in a large, community-based cancer study cohort. Cancer, 123(2), 327–335.CrossRefGoogle Scholar
Kaat, A. J., Kallen, M. A., Nowinski, C. J., Sterling, S. A., Westbrook, S. R., Peters, J. T.PROMIS® pediatric depressive symptoms as a harmonized score metric.Journal of Pediatric Psychology,(2020).45 3271–280CrossRefGoogle ScholarPubMed
Kaat, A. J. Newcomb, M. E. Ryan, D. T. & Mustanski, B. (2017). Expanding a common metric for depression reporting: linking two scales to PROMIS® depression. Quality of Life Research, 26 (5), 1119–1128CrossRefGoogle ScholarPubMed
Kang, T, Petersen, N. S.Linking item parameters to a base scale.Asia Pacific Education Review,(2012).13 2311–321CrossRefGoogle Scholar
Katzan, I. L., Fan, Y, Griffith, S. D., Crane, P. K., Thompson, N. R., Cella, DScale linking to enable patient-reported outcome performance measures assessed with different patient-reported outcome measures.Value in Health,(2017).20 81143–1149CrossRefGoogle ScholarPubMed
Kim, J. Chung, H. Askew, R. L. Park, R. Jones, S. M. Cook, K. F. & Amtmann, D. (2015). Translating CESD-20 and PHQ-9 scores to PROMIS depression. Assessment, 1073191115607042.Google Scholar
Kim, SA comparative study of IRT fixed parameter calibration methods.Journal of Educational Measurement,(2006).43 4355–381CrossRefGoogle Scholar
Kolen, M. J., Brennan, R. L.Test equating, scaling, and linking: Methods and practices,(2014).Berlin:SpringerCrossRefGoogle Scholar
Kroenke, K, Spitzer, R. L., Williams, J. B.The PHQ-9: Validity of a brief depression severity measure.Journal of General Internal Medicine,(2001).16 9606–6131495268CrossRefGoogle ScholarPubMed
Kroenke, K, Spitzer, R. L., Williams, J. B., Löwe, BThe patient health questionnaire somatic, anxiety, and depressive symptom scales: A systematic review.General Hospital Psychiatry,(2010).32 4345–359CrossRefGoogle ScholarPubMed
Lai, J-S, Cella, D, Yanez, B, Stone, ALinking fatigue measures on a common reporting metric.Journal of Pain and Symptom Management,(2014).48 4639–6484185006CrossRefGoogle ScholarPubMed
Lee, W. C. & Lee, G. (2018). IRT linking and equating (pp. 639–673). The Wiley Handbook of Psychometric Testing: A Multidisciplinary Reference on Survey, Scale and Test Development.Google Scholar
Liegl, G, Wahl, I, Berghöfer, A, Nolte, S, Pieh, C, Rose, M, Fischer, FUsing Patient Health Questionnaire-9 item parameters of a common metric resulted in similar depression scores compared to independent item response theory model reestimation.Journal of Clinical Epidemiology,(2016).71, 25–34CrossRefGoogle ScholarPubMed
Liu, H, Cella, D, Gershon, R, Shen, J, Morales, L. S., Riley, W, Hays, R. D.Representativeness of the patient-reported outcomes measurement information system internet panel.Journal of Clinical Epidemiology,(2010).63 111169–11782943555CrossRefGoogle ScholarPubMed
Lord, F. M.Applications of item response theory to practical testing problems,(1980).London:RoutledgeGoogle Scholar
Lord, F. M.The standard error of equipercentile equating.Journal of Educational Statistics,(1982).7 3165–174CrossRefGoogle Scholar
Lord, F. M., Wingersky, M. S.Comparison of IRT true-score and equipercentile observed-score equatings.Applied Psychological Measurement,(1984).8 4453–461CrossRefGoogle Scholar
Lucke, JF (2015). Unipolar item response models. In Reise, S. P. & Revicki, D. A. (Eds.), Handbook of Item Response Theory Modeling: Applications to Typical Performance Assessment (pp. 272–284). New York, NY: Routledge/Taylor & Francis Group.Google Scholar
Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J, Stratford, P. W., Knol, D. L., De Vet, H. C.The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: An international Delphi study.Quality of Life Research,(2010).19 4539–5492852520CrossRefGoogle ScholarPubMed
McHugh, R. K. Rasmussen, J. L. & Otto, M. W. (2011). Comprehension of self-report evidence-based measures of anxiety. Depression and Anxiety, 28 (7), 607–614.CrossRefGoogle ScholarPubMed
Park, T, Reilly-Spong, M, Gross, C. R.Mindfulness: A systematic review of instruments to measure an emergent patient-reported outcome (PRO).Quality of Life Research,(2013).22 102639–2659CrossRefGoogle ScholarPubMed
Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., Cella, DItem banks for measuring emotional distress from the patient-reported outcomes measurement information system (PROMIS®): Depression, anxiety, and anger.Assessment,(2011).18 3263–2833153635CrossRefGoogle ScholarPubMed
Pilkonis, P. A., Choi, S. W., Salsman, J. M., Butt, Z, Moore, T. L., Lawrence, S. M., Knox, S. S.Assessment of self-reported negative affect in the NIH Toolbox.Psychiatry Research,(2013).206 188–97CrossRefGoogle ScholarPubMed
Pilkonis, P. A., Yu, L, Dodds, N. E., Johnston, K. L., Maihoefer, C. C., Lawrence, S. M.Validation of the depression item bank from the patient-reported outcomes measurement information system (PROMIS®) in a three-month observational study.Journal of Psychiatric Research,(2014).56, 112–1194096965CrossRefGoogle Scholar
Purvis, T. E., Neuman, B. J., Riley, L. H. III, Skolasky, R. L.Discriminant ability, concurrent validity, and responsiveness of PROMIS health domains among patients with lumbar degenerative disease undergoing decompression with or without arthrodesis.Spine,(2018).43 211512–1520CrossRefGoogle ScholarPubMed
Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., Hambleton, R. K.Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the patient-reported outcomes measurement information system (PROMIS).Medical Care,(2007).45 5S22–S31CrossRefGoogle ScholarPubMed
Reeve, B. B., Thissen, D, DeWalt, D. A., Huang, I-C, Liu, Y, Magnus, B, Ni, PLinkage between the PROMIS®pediatric and adult emotional distress measures.Quality of Life Research,(2016).25 4823–833CrossRefGoogle Scholar
Reinsch, C. H.Smoothing by spline functions.Numerische mathematik,(1967).10 3177–183CrossRefGoogle Scholar
Reise, S. P. Moore, T. M. & Haviland, M. G. (2013). Applying unidimensional item response theory models to psychological data. In K. F. Geisinger, B. A. Bracken, J. F. Carlson, J.-I. C. Hansen, N. R. Kuncel, S. P. Reise, & M. C. Rodriguez (Eds.), APA handbooks in psychology®. APA handbook of testing and assessment in psychology, Vol. 1. Test theory and testing and assessment in industrial and organizational psychology (p. 101–119). American Psychological Association.CrossRefGoogle Scholar
Reise, S. P., Rodriguez, A, Spritzer, K. L., Hays, R. D.Alternative approaches to addressing non-normal distributions in the application of IRT models to personality measures.Journal of Personality Assessment,(2018).100 4363–374CrossRefGoogle ScholarPubMed
Revicki, D, Hays, R. D., Cella, D, Sloan, JRecommended methods for determining responsiveness and minimally important differences for patient-reported outcomes.Journal of Clinical Epidemiology,(2008).61 2102–109CrossRefGoogle ScholarPubMed
Rose, J. S., Dierker, L. C., Hedeker, D, Mermelstein, RAn integrated data analysis approach to investigating measurement equivalence of DSM nicotine dependence symptoms.Drug and Alcohol Dependence,(2013).129 1–225–32CrossRefGoogle ScholarPubMed
Rose, M, Bjorner, J. B., Gandek, B, Bruce, B, Fries, J. F., Ware, J. E.The PROMIS physical function item bank was calibrated to a standardized metric and shown to improve measurement efficiency.Journal of Clinical Epidemiology,(2014).67 5516–5264465404CrossRefGoogle ScholarPubMed
Rosseel, YLavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA).Journal of Statistical Software,(2012).48 21–36CrossRefGoogle Scholar
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. (Psychometrika Monograph Supplement No. 17) Richmond, VA Psychometrics Society.CrossRefGoogle Scholar
Schalet, B. D., Cook, K. F., Choi, S. W., Cella, DEstablishing a common metric for self-reported anxiety: Linking the MASQ, PANAS, and GAD-7 to PROMIS Anxiety.Journal of Anxiety Disorders,(2014).28 188–96CrossRefGoogle ScholarPubMed
Schalet, B. D. Janulis, P., Kipke, M. D. Mustanski, B. Shoptaw, S. Moore, R. et al. (2020). Psychometric Data Linking Across HIV and Substance Use Cohorts. AIDS and Behavior, 24, 3215–3224.CrossRefGoogle ScholarPubMed
Segawa, E, Schalet, B, Cella, DA comparison of computer adaptive tests (CATs) and short forms in terms of accuracy and number of items administrated using PROMIS profile.Quality of Life Research,(2020).29 1213–221CrossRefGoogle ScholarPubMed
Stocking, M. L., Lord, F. M.Developing a common metric in item response theory.Applied Psychological Measurement,(1983).7 2201–210CrossRefGoogle Scholar
ten Klooster, P. M., Voshaar, MA. O., Gandek, B, Rose, M, Bjorner, J. B., Taal, E, van de Laar, M. A.Development and evaluation of a crosswalk between the SF-36 physical functioning scale and Health Assessment Questionnaire disability index in rheumatoid arthritis.Health and Quality of Life Outcomes,(2013).11 11CrossRefGoogle ScholarPubMed
Thissen, D., Liu, Y., Magnus, B., Quinn, H. (2015) Extending the Use of Multidimensional IRT Calibration as Projection: Many-to-One Linking and Linear Computation of Projected Scores. In van der Ark L., Bolt D., Wang WC., Douglas J., Chow SM. (Eds.), Quantitative Psychology Research. Springer Proceedings in Mathematics & Statistics, vol 140 (pp 1–16). Springer, Cham.CrossRefGoogle Scholar
Thissen, D, Pommerich, M, Billeaud, K, Williams, V. S.Item response theory for scores on tests including polytomous items with ordered responses.Applied Psychological Measurement,(1995).19 139–49CrossRefGoogle Scholar
Thissen, D, Varni, J. W., Stucky, B. D., Liu, Y, Irwin, D. E., DeWalt, D. A.Using the PedsQL™3.0 asthma module to obtain scores comparable with those of the PROMIS pediatric asthma impact scale (PAIS).Quality of Life Research,(2011).20 91497–15053196830CrossRefGoogle Scholar
Tomitaka, S, Kawasaki, Y, Ide, K, Akutagawa, M, Ono, Y, Furukawa, T. A.Distribution of psychological distress is stable in recent decades and follows an exponential pattern in the US population.Scientific Reports,(2019).9 11–10CrossRefGoogle ScholarPubMed
Tuck, N. L., Johnson, M. H., Bean, D. J.You’d better believe it: The conceptual and practical challenges of assessing malingering in patients with chronic pain.The Journal of Pain,(2019).20 2133–145CrossRefGoogle ScholarPubMed
Tulsky, D. S., Kisala, P. A., Boulton, A. J., Jette, A. M., Thissen, D, Ni, P, Mulcahey, MDetermining a transitional scoring link between PROMIS® pediatric and adult physical health measures.Quality of Life Research,(2019).28 51217–1229CrossRefGoogle ScholarPubMed
Uijen, A. A., Heinst, C. W., Schellevis, F. G., van den Bosch, W. J., van de Laar, F. A., Terwee, C. B.et al..Measurement properties of questionnaires measuring continuity of care: A systematic review.PloS One,(2012).7 7e422563409169CrossRefGoogle ScholarPubMed
Victorson, D, Schalet, B. D., Kundu, S, Helfand, B. T., Novakovic, K, Penedo, F, Cella, DEstablishing a common metric for self-reported anxiety in patients with prostate cancer: Linking the Memorial Anxiety Scale for Prostate Cancer with PROMIS Anxiety.Cancer,(2019).125 183249–3258CrossRefGoogle ScholarPubMed
von Davier, M, Yamamoto, K, Shin, H. J., Chen, H, Khorramdel, L, Weeks, J, Kandathil, MEvaluating item response theory linking and model fit for data from PISA 2000–2012.Assessment in Education: Principles, Policy and Practice,(2019).26 4466–488Google Scholar
Voshaar, M. O., Vonkeman, H, Courvoisier, D, Finckh, A, Gossec, L, Leung, Y, Wulfraat, NTowards standardized patient reported physical function outcome reporting: Linking ten commonly used questionnaires to a common metric.Quality of Life Research,(2019).28 1187–197CrossRefGoogle Scholar
Wall, M. M., Park, J. Y., Moustaki, IIRT modeling in the presence of zero-inflation with application to psychiatric disorder severity.Applied Psychological Measurement,(2015).39 8583–5975978495CrossRefGoogle ScholarPubMed
Supplementary material: File

Schalet et al. supplementary material

Online Resource 1
Download Schalet et al. supplementary material(File)
File 78.6 KB
Supplementary material: File

Schalet et al. supplementary material 1

Schalet et al. supplementary material 1
Download Schalet et al. supplementary material 1(File)
File 4.5 MB
Supplementary material: File

Online Resource 3

Online Resource 3
Download Online Resource 3(File)
File 46.4 KB