Advances in the Evaluation of Longitudinal Construct Validity of Clinical Outcome Assessments

Therapeutic Innovation and Regulatory Science

Volumn 49, Issue 6, 2015, Pages 805-812

  Williams, Valerie ^a   McLeod, Lori ^a   Nelson, Lauren ^a  

^a RTI HEALTH SOLUTIONS   (United States)

Author keywords

clinical outcome assessment; longitudinal construct validity; psychometrics; responsiveness; validation

Indexed keywords

ARTICLE; CLINICAL ASSESSMENT; CLINICAL EVALUATION; CLINICAL TRIAL (TOPIC); CONSTRUCT VALIDITY; CORRELATIONAL STUDY; CROSS-SECTIONAL STUDY; HUMAN; KNOWN GROUP ANALYSIS; LONGITUDINAL CONSTRUCT VALIDITY; LONGITUDINAL STUDY; OUTCOME ASSESSMENT; PRIORITY JOURNAL; PSYCHOMETRY;

EID: 84944680166     PISSN: 21684790     EISSN: 21684804     Source Type: Journal    
DOI: 10.1177/2168479015609103     Document Type: Article

References (57)

1. J.C.CappelleriK.H.ZouA.G.BushmakinJ.M.J.AlvirD.AlemayehuT.Symonds. Patient-Reported Outcomes Measurement, Implementation, and Interpretation. London: Chapman & Hall/CRC; 2014.
2. R.D.HaysD.Revicki. Reliability and validity (including responsiveness). In: P.FayersR.Hays, eds. Assessing Quality of Life in Clinical Trials. 2nd ed. New York: Oxford University Press; 2005:25–40.
3. R.D.HaysR.T.AndersonD.Revicki. Assessing reliability and validity of measurement in clinical trials. In: M.J.StaquetR.D.HaysP.M.Fayers, eds. Quality of Life Assessment in Clinical Trials. New York: Oxford University Press; 1998:169–182.
4. J.A.HustedR.J.CookV.T.FarewellD.D.Gladman. Methods for assessing responsiveness: a critical review and recommendations. J Clin Epidemiol. 2000;53:459–468.
5. B.B.ReeveK.W.WyrwichA.W.Wu. ISOQOL recommends minimum standards for patient-reported outcome measures used in patient-centered outcomes and comparative effectiveness research. Qual Life Res. 2013;22:1889–1905.
6. AERA, APA, NCME. Standards for Educational and Psychological Testing. Washington, DC: AERA; 2014.
7. D.BorsboomG.J.MellenberghJ.van Heerden. The concept of validity. Psychol Bull. 2004;111:1061–1071.
8. L.J.CronbachP.E.Meehl. Construct validity in psychological tests. Psychol Bull. 1955;52:281–302.
9. H.C.W.De VetC.B.TerweeL.B.MokkinkD.L.Knol. Measurement in Medicine. New York: Cambridge University Press; 2011.
10. I.McDowellC.Newell. Measuring Health—A Guide to Rating Scales and Questionnaires. 2nd ed. New York: Oxford University Press; 1996.
11. S.Messick. Validity. In: R.L.Linn, ed. Educational Measurement. 3rd ed. New York: American Council on Education, Macmillan; 1989:150–170.
12. J.C.NunnallyI.H.Bernstein. Psychometric Theory. 3rd ed. New York: McGraw-Hill; 1994.
13. H.WainerH.I.Braun, eds. Test Validity. Hillsdale, NJ: Lawrence Erlbaum; 1988.
14. Food and Drug Administration. Guidance for industry. Patient-reported outcome measures: use in medical product development to support labeling claims. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM193282.pdf. Published December 2009. Accessed December 31, 2013.
15. L.Cronbach. Five perspectives on validity argument. In: H.WainerH.I.Braun, eds. Test Validity. Hillsdale, NJ: Lawrence Erlbaum; 1988:3–17.
16. Scientific Advisory Committee of the Medical Outcomes Trust. Assessing health status and quality-of-life instruments: attributes and review criteria. Qual Life Res. 2002;11:193–205.
17. H.K.Suen. Principles of Test Theories. Hillsdale, NJ: Lawrence Erlbaum; 1990.
18. J.C.CappelleriA.G.BushmakinJ.HarnessC.Mamolo. Psychometric validation of the physician global assessment scale for assessing severity of psoriasis disease activity. Qual Life Res. 2013;22:2489–2499.
19. L.B.MokkinkC.B.TerweeD.L.Patrick. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol. 2010;63:737–745.
20. G.FitzmauriceM.DavidianG.VerbekeG.Molenberghs, eds. Longitudinal Data Analysis. Boca Raton, FL: Chapman & Hall/CRC; 2009.
21. M.DavidianD.M.Giltinan. Nonlinear Models for Repeated Measurement Data. Boca Raton, FL: Chapman & Hall/CRC; 1995.
22. K.J.Grimm. Multivariate longitudinal methods for studying developmental relationships between depression and academic achievement. Int J Behav Dev. 2007;31:328–339.
23. D.E.Stull. Analyzing growth and change: latent variable growth curve modeling with an application to clinical trials. Qual Life Res. 2008;17:47–59.
24. K.J.Preacher. Latent Growth Curve Modeling. Thousand Oaks, CA: Sage; 2008.
25. T.E.DuncanS.C.Duncan. The ABC’s of LGM: an introductory guide to latent variable growth curve modeling. Soc Personal Psychol Compass. 2009;3:979–991.
26. J.D.SingerJ.B.Willett. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press; 2003.
27. D.L.Fairclough. Design and Analysis of Quality of Life Studies in Clinical Trials. 2nd ed. Boca Raton, FL: Chapman & Hall/CRC; 2010.
28. J.C.CappelleriA.G.Bushmakin. Interpretation of patient-reported outcomes. Stat Methods Med Res. 2014;23:460–483.
29. C.H.MallinckrodtP.W.LaneD.SchnellY.PengJ.P.Mancuso. Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials. Drug Inform J. 2008;42:305–319.
30. J.Cohen. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum; 1988.
31. D.KhannaP.MaranianN.Rothrock. Feasibility and construct validity of PROMIS and legacy instruments in an academic scleroderma clinic—analysis from the UCLA scleroderma quality of life study. Value Health. 2012;15:128–134.
32. G.Y.Zou. Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Stat Med. 2012;31:3972–3981.
33. J.A.NelderR.W.M.Wedderburn. Generalized linear model. J Royal Stat Soc A. 1972;135:370–384.
34. G.M.FitzmauriceN.M.LairdJ.H.Ware. Applied Longitudinal Analysis. 2nd ed. Hoboken, NJ: John Wiley; 2011.
35. C.FaesH.GeysP.Catalan. Joint models for continuous and discrete longitudinal data. In: G.FitzmauriceM.DavidianG.VerbekeG.Molenberghs, eds. Longitudinal Data Analysis. Boca Raton FL: Chapman & Hall/CRC; 2009.
36. D.OdomL.McLeodB.SherifL.NelsonD.McSorley. Methods to assess the association of patient-reported outcomes and clinical endpoints. Presented at: 20th Annual International Meeting of the International Society for Pharmacoeconomics and Outcomes Research; May 18, 2015; Philadelphia, PA.
37. K.A.BollenP.J.Curran. Latent Curve Models: A Structural Equation Perspective. New York, NY: John Wiley; 2006.
38. W.MeredithJ.Tisak. Latent curve analysis. Psychometrika. 1990;55:107–122.
39. R.J.SwartzC.SchwartzE.Basch. The king’s foot of patient reported outcomes: current practices and new developments for the measurement of change. Qual Life Res. 2011;20:1159–1167.
40. P.J.CurranK.ObeidatD.Losardo. Twelve frequently asked questions about growth curve modeling. J Cogn Dev. 2010;11:121–136.
41. R.F.DeVellis. Scale Development—Theory and Applications. 2nd ed. Thousand Oaks, CA: Sage; 2003.
42. C.B.TerweeS.D.M.BotM.R.de Boer. Quality criteria were proposed for measurement properties of health status measures. J Clin Epidemiol. 2007;60:34–42.
43. F.M.FayersD.Machin. Quality of Life: The Assessment, Analysis and Interpretation of Patient-Reported Outcomes. 2nd ed. Chichester, England: John Wiley; 2007.
44. M.BrussoniS.KruseK.Walker. Validity and reliability of the EQ-5D-3L™ among a paediatric injury population. Health Qual Life Outcomes. 2013;11:157. doi:10.1186/1477-7525-11-157.
45. E.AnthoineL.MoretA.RegnaultV.SebilleJ.Hardouin. Sample size used to validate a scale: a review of publications on newly developed patient-reported outcome measures. HQOL Outcomes. 2014;12:176. doi:10.1186/s12955-014-0176-2.
46. D.E.BeatonC.BombardierJ.N.KatzJ.G.Wright. A taxonomy of responsiveness. J Clin Epidemiol. 2001;54:1204–1217.
47. R.A.DeyoP.DiehrD.L.Patrick. Reproducibility and responsiveness of health status measures. Control Clin Trials. 1991;12:142S–158S.
48. M.H.Liang. Longitudinal construct validity: establishment of clinical meaning in patient evaluative instruments. Med Care. 2000;38(9 suppl):II84–II90.
49. G.Y.Zou. Quantifying responsiveness of quality of life measures without an external criterion. Qual Life Res. 2005;14:1545–1552.
50. G.R.NormanK.W.WyrwichD.L.Patrick. The mathematical relationship among different forms of responsiveness coefficients. Qual Life Res. 2007;16:815–822.
51. C.B.TerweeF.W.DekkerW.M.WiersingaM.F.PrummelP.M.M.Bossuyt. On assessing responsiveness of health-related quality of life instruments: guidelines for instrument evaluation. Qual Life Res. 2003;12:349–362.
52. G.H.GuyattS.D.WalterG.Norman. Measuring change over time: assessing the usefulness of evaluative instruments. J Chron Dis. 1987;40:171–178.
53. L.E.KazisJ.J.AndersonR.F.Meenan. Effect sizes for interpreting changes in health status. Med Care. 1989;27(suppl):S178–S189.
54. J.C.CappelleriA.G.BushmakinA.M.McDermottA.B.SadoskyC.D.PetrieS.Martin. Psychometric properties of a single-item scale to assess sleep quality among individuals with fibromyalgia. Health Qual Life Outcomes. 2009;7:54–60.
55. L.M.NelsonV.S.L.WilliamsS.E.Fehnel. Psychometric validation of patient-reported outcome measures assessing chronic constipation. Clin Exp Gastroenterol. 2014;7:385–394.
56. V.S.L.WilliamsR.J.MorlockD.Feltner. Psychometric evaluation of a visual analog scale for the assessment of anxiety. Health Qual Life Outcomes. 2010;8:57.
57. W.C.ChenL.D.McLeodL.M.NelsonV.S.WilliamsS.E.Fehnel. Quantitative challenges facing patient-centered outcomes research. Expert Rev Pharmacoecon Outcomes Res. 2014;14:379–386.