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Use of “spydergrams” to present and interpret SF-36 health-related quality of life data across rheumatic diseases
  1. V Strand1,
  2. B Crawford2,
  3. J Singh3,4,5,
  4. E Choy6,
  5. J S Smolen7,
  6. D Khanna8
  1. 1
    Division of Immunology/Rheumatology, Stanford University School of Medicine, Stanford, California, USA
  2. 2
    Mapi Values, Boston, Massachusetts, USA
  3. 3
    Rheumatology Section, Medicine Service, VA Medical Center, Minneapolis, Minnesota, USA
  4. 4
    Division of Rheumatology, Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
  5. 5
    Departments of Health Sciences and Orthopaedic Surgery, Mayo Clinic College of Medicine, Rochester, Minnesota, USA
  6. 6
    Sir Alfred Baring Garrod Clinical Trials Unit, Academic Department of Rheumatology, King’s College London, London UK
  7. 7
    Division of Rheumatology, Department of Medicine 3, Medical University of Vienna, Vienna, Austria
  8. 8
    Division of Rheumatology, Department of Medicine, David Geffen School of Medicine, Los Angeles, California, USA
  1. Correspondence to Dr V Strand, 306 Ramona Road, Portola Valley, CA 94028, USA; vstrand{at}


The Medical Outcomes Study Short Form-36 (SF-36) is a generic measure of health-related quality of life (HRQOL), validated and cross-culturally translated, which has been extensively utilised in rheumatology. In randomised controlled trials and observational studies, SF-36 provides rich data regarding HRQOL; but as typically portrayed, patterns of disease and treatment-associated effects can be difficult to discern. “Spydergrams” offer a simplified means to visualise complex results across all domains of SF-36 in a single figure: depicting disease and population-specific patterns of decrements in HRQOL compared with age and gender-matched normative data, as well as providing a tool for interpreting complex treatment-associated or longitudinal changes. Utilising spydergrams as a standard format to illustrate and report changes in SF-36 across different rheumatic diseases can greatly facilitate analyses and interpretations of clinical trial results, as well as providing patients an accessible means to compare baseline scores and treatment-associated improvements with normative data from individuals without arthritis. Furthermore, SF-6D utility scores based on mean changes across all eight domains of SF-36 are suggested as a quantitative means of summarising changes illustrated by spydergrams, offering a universal metric for cost-effectiveness analyses of therapeutic interventions.

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  • Funding DK was supported by a National Institutes of Health Award (NIAMS K23 AR053858-01A1) and the Scleroderma Foundation (New Investigator Award).

  • Competing interests VS is a consultant to the following: Abbott Immunology, Alder, Allergan, Almirall, Amgen Corporation, AstraZeneca, Bexel, BiogenIdec, CanFite, Centocor, Chelsea, Crescendo, Cypress Biosciences, Eurodiagnostica, Fibrogen, Forest Laboratories, Genentech, Human Genome Sciences, Idera, Incyte, Jazz Pharmaceuticals, Lexicon Genetics, Logical Therapeutics, Lux Biosciences, Medimmune, Merck Serono, Novartis Pharmaceuticals, NovoNordisk, Nuon, Ono Pharmaceuticals, Pfizer, Procter and Gamble, Rigel, Roche, Sanofi-Aventis, Savient, Schering Plough, SKK, UCB, Wyeth and Xdx. The other authors declare no conflicts of interest.

  • Provenance and Peer review Not commissioned; externally peer reviewed.

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