Quality of Life Research

, Volume 22, Issue 9, pp 2489–2499 | Cite as

Psychometric validation of the physician global assessment scale for assessing severity of psoriasis disease activity

  • J. C. Cappelleri
  • A. G. Bushmakin
  • J. Harness
  • C. Mamolo
Article

Abstract

Purpose

The Physician Global Assessment (PGA) is a key measure of psoriasis frequently used in clinical trials. A psychometric validation of a three-item (erythema, induration, and scaling) PGA scale was performed using Phase 2 data.

Methods

Confirmatory factor analysis (CFA) tested the PGA measurement model and appropriateness of equal weighting of the items. PGA test–retest reliability was assessed by estimating the intraclass correlation coefficient (ICC). Internal consistency reliability was gauged by calculating Cronbach’s coefficient α (CCα). Clinically important difference (CID) was defined using the repeated measures model to estimate the relationship between PGA and Patient Global Assessment (PtGA). Known-group, convergent, and divergent validity for the PGA were also assessed.

Results

197 patients with psoriasis were randomized to tofacitinib 2, 5, 15 mg twice daily, or placebo. CFA demonstrated that the PGA measurement model fitted the data using equal weighting of the PGA items. The PGA scale demonstrated good test–retest reliability (ICC > 0.7) and internal consistency reliability (CCα > 0.8). The CID for PGA was estimated at 0.52 (95 % confidence interval: 0.47, 0.56). A robust monotonic relationship between PGA and Psoriasis Area and Severity Index (PASI) data substantiated known-group validity. Relatively high correlations of PGA with PASI and PtGA data (all correlations >0.5 except at baseline) supported convergent validity; relatively low correlations of PGA with the Pain/Discomfort Assessment and the Ocular Comfort Index supported divergent validity.

Conclusions

The three-item PGA scale has sound psychometric properties with respect to reliability and validity, with equal weighting of the items being appropriate.

Keywords

Physician Global Assessment Psoriasis Validation 

References

  1. 1.
    Fredriksson, T., & Pettersson, U. (1978). Severe psoriasis—Oral therapy with a new retinoid. Dermatologica, 157, 238–244.PubMedCrossRefGoogle Scholar
  2. 2.
    Feldman, S. R., & Krueger, G. G. (2005). Psoriasis assessment tools in clinical trials. Annals of the Rheumatic Diseases, 64(Suppl 2), ii65–ii68.PubMedGoogle Scholar
  3. 3.
    European Medicines Agency. (2004). Guideline on clinical investigation of medicinal products indicated for the treatment of psoriasis.Google Scholar
  4. 4.
    Ashcroft, D. M., Wan Po, A. L., Williams, H. C., & Griffiths, C. E. (1999). Clinical measures of disease severity and outcome in psoriasis: A critical appraisal of their quality. British Journal of Dermatology, 141, 185–191.PubMedCrossRefGoogle Scholar
  5. 5.
    Langley, R. G., & Ellis, C. N. (2004). Evaluating psoriasis with psoriasis area and severity index, psoriasis global assessment, and lattice system physician’s global assessment. Journal of the American Academy of Dermatology, 51, 563–569.PubMedCrossRefGoogle Scholar
  6. 6.
    Berth-Jones, J., Grotzinger, K., Rainville, C., et al. (2006). A study examining inter- and intrarater reliability of three scales for measuring severity of psoriasis: Psoriasis area and severity index, physician’s global assessment and lattice system physician’s global assessment. British Journal of Dermatology, 155, 707–713.PubMedCrossRefGoogle Scholar
  7. 7.
    Spuls, P. I., Lecluse, L. L. A., Poulsen, M.-L., Bos, J. D., Stern, R. S., & Nijsten, T. (2010). How good are clinical severity and outcome measures for psoriasis? Quantitative evaluation in a systematic review. Journal of Investigative Dermatology, 130, 933–943.PubMedCrossRefGoogle Scholar
  8. 8.
    Naldi, L. (2010). Scoring and monitoring the severity of psoriasis. What is the preferred method? What is the ideal method? Is PASI passé? facts and controversies. Clinics in Dermatology, 28(1), 67–72.PubMedCrossRefGoogle Scholar
  9. 9.
    Naldi, L., Svensson, A., Diepgen, T., et al. (2003). Randomized clinical trials for psoriasis 1977–2000: The EDEN survey. Journal of Investigative Dermatology, 120, 738–741.PubMedCrossRefGoogle Scholar
  10. 10.
    Robinson, A., Kardos, M., & Kimball, A. B. (2012). Physician global assessment and psoriasis area and severity index: Why do both? A systematic analysis of randomized controlled trials of biologics agents for moderate to severe plaque psoriasis. Journal of the American Academy of Dermatology, 66, 369–375.PubMedCrossRefGoogle Scholar
  11. 11.
    Puzenat, E., Bronsard, V., Prey, S., et al. (2010). What are the best outcome measures for assessing plaque psoriasis severity? A systematic review of the literature. Journal of the European Academy of Dermatology and Venereology, 24(Suppl 2), 10–16.PubMedCrossRefGoogle Scholar
  12. 12.
    Stern, R. S. (2010). Poor metrics and lost opportunity. Journal of the American Academy of Dermatology, 63, 718–719.PubMedCrossRefGoogle Scholar
  13. 13.
    Sloan, J. A., Aaronson, N., Cappelleri, J. C., et al. (2002). Assessing the clinical significance of single items relative to summated scores. Mayo Clinic Proceedings, 77, 479–487.PubMedGoogle Scholar
  14. 14.
    Papp, K., Menter, A., Strober, B., et al. (2012). Efficacy and safety of tofacitinib, an oral JAK inhibitor, in the treatment of psoriasis: A Phase 2b randomised placebo-controlled dose-ranging study. British Journal of Dermatology, 167, 668–677.PubMedCrossRefGoogle Scholar
  15. 15.
    Garduno, J., Bhosle, M. J., Balkrishnan, R., & Feldman, S. R. (2007). Measures used in specifying psoriasis lesion(s), global disease and quality of life: A systematic review. Journal of Dermatological Treatment, 18, 223–242.PubMedCrossRefGoogle Scholar
  16. 16.
    Karabulut, A. A., Yalvac, I. S., Vahaboglu, H., et al. (1999). Conjunctival impression cytology and tear-film changes in patients with psoriasis. Cornea, 18, 544–548.PubMedCrossRefGoogle Scholar
  17. 17.
    Johnson, M. E., & Murphy, P. J. (2007). Measurement of ocular surface irritation on a linear interval scale with the ocular comfort index. Investigative Ophthalmology & Visual Science, 48, 4451–4458.CrossRefGoogle Scholar
  18. 18.
    Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.PubMedCrossRefGoogle Scholar
  19. 19.
    Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: The Guilford Press.Google Scholar
  20. 20.
    Hatcher, L. (1994). A step-by-step approach to using the SAS ® system for factor analysis and structural equation modeling. Cary, Nc: SAS Institute Inc.Google Scholar
  21. 21.
    Kline, R. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: The Guilford Press.Google Scholar
  22. 22.
    Fayers, F. M., & Machin, D. (2007). Quality of life: The assessment, analysis, and interpretation of patient-reported outcomes (2nd ed.). Chichester, England: Wiley.Google Scholar
  23. 23.
    Cappelleri, J. C., & Bushmakin, A. G. (2013). Interpretation of patient-reported outcomes. Statistical Methods in Medical Research (in press).Google Scholar
  24. 24.
    King, M. T. (2011). A point of minimal important difference (MID): A critique of terminology and methods. Expert Review of Pharmacoeconomics Outcomes Research, 11, 171–184.PubMedCrossRefGoogle Scholar
  25. 25.
    Mulhall, J. O., Goldstein, I., Bushmakin, A., et al. (2007). Validation of the erectile hardness score. The Journal of Sexual Medicine, 4, 1626–1634.PubMedCrossRefGoogle Scholar
  26. 26.
    Deyo, R. A., Inui, T. S., Leininger, J., et al. (1982). Physical and psychosocial function in rheumatoid arthritis: Clinical use of a self-administered health status instrument. Archives of Internal Medicine, 142, 879–882.PubMedCrossRefGoogle Scholar
  27. 27.
    Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  28. 28.
    Stevens, J. (2002). Applied multivariate statistics for the social sciences (4th ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  29. 29.
    Copay, A. G., Subach, B. R., Glassman, S. D., et al. (2007). Understanding the minimum clinically important difference: A review of concepts and methods. The Spine Journal, 7, 541–546.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • J. C. Cappelleri
    • 1
  • A. G. Bushmakin
    • 1
  • J. Harness
    • 2
  • C. Mamolo
    • 3
  1. 1.Pfizer IncGrotonUSA
  2. 2.Novartis Pharma AGBaselSwitzerland
  3. 3.Pfizer IncGrotonUSA

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