Leading Article Determining Clinical Differences in Health Status
The objective of this article was to describe and illustrate a comprehensive approach for estimating clinically important differences (CIDs) in health-related quality-of-life (HR-QOL). A literature review and pilot study were conducted to determine whether effect size-based benchmarks are consistent with CIDs obtained from other approaches.
CIDs may be estimated based primarily upon effect sizes, supplemented by more traditional anchor-based methods of benchmarking (i.e. direct, cross-sectional or longitudinal approaches). A literature review of articles discussing CIDs provided comparative data on effect sizes for various chronic conditions. A pilot study was then conducted to estimate the minimum CID of the Health Utilities Index (HUI) Mark II, and to compare the observed between-group differences observed in a recent randomised trial of an acute stroke intervention with this benchmark.
The use of standardised effect size benchmarks has a number of advantages–for example, effect sizes are efficient, widely accepted outside HR-QOL, and have well accepted benchmarks based upon external anchors. In addition, our literature review and pilot study suggest that effect size-based CID benchmarks are similar to those which would be obtained using more traditional methods. For most HR-QOL instruments, we do not know the changes in score which constitute CIDs of various magnitudes. This makes interpretation of HR-QOL results from clinical trials difficult, and having a benchmarking process which is relatively straightforward would be highly desirable.
Adis International Limited Physical Component Summary Health Utility Index Health Utility Index Mark Sickness Impact Profile
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.
Guyatt GH, Jaeschke RJ. Reassessing quality-of-life instruments in the evaluation of new drugs. Pharmacoeconomics 1997; 12 (6): 616–26CrossRefGoogle Scholar
McHorney CA, Ware JE, Lu JFR, et al. The MOS 36-item shortform health survey (SF-36): III. tests of data quality, scaling assumptions, and reliability across diverse patient groups. Med Care 1994; 32: 40–66PubMedCrossRefGoogle Scholar
Torrance GW, Feeny DH, Furlong WJ, et al. Multiattribute utility function for a comprehensive health status classification system: health utilities index mark 2. Med Care 1996; 34: 702–22PubMedCrossRefGoogle Scholar
Guyatt G, Walter S, Normal G. Measuring change over time: assessing the usefulness of evaluative instruments. J Chronic Dis 1987; 40: 171–8PubMedCrossRefGoogle Scholar
Ware JE, Kosinski M, Keller SD. SF-36 physical and mental health summary scales: a user’s manual. Boston (MA): The Health Institute New England Medical Center, 1994Google Scholar
Deyo RA, Patrick DL. The significance of treatment effects: the clinical perspective. Med Care 1995; 33 (4): AS285–91Google Scholar
Llewellyn-Thomas HA, Williams JI, et al. Using a trade-off technique to assess patients’ treatment preferences for benign prostatic hyperplasia. Med Decis Making 1996; 16: 262–72PubMedCrossRefGoogle Scholar
Stewart AL, Greenfield S, Hays RD, et al. Functional status and well-being of patients with chronic conditions: results from the medical outcomes study. JAMA 1989; 262: 907–13PubMedCrossRefGoogle Scholar
Thompson MS, Read JL, Hutchings HC, et al. The cost effectiveness of auranofin: results of a randomized clinical trial. J Rheumatol 1988; 15: 35–42PubMedGoogle Scholar
Juniper EF, Guyatt GH, Willan A, et al. Determining a minimal important change in a disease-specific quality of life questionnaire. J Clin Epidemol 1994; 47: 81–7CrossRefGoogle Scholar
Kazis LE, Anderson JJ, Meenan RS. Effect sizes for interpreting changes in health status. Med Care 1989; 27 Suppl. 3: S178–89CrossRefGoogle Scholar
Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale (NJ): Lawrence Erlbaum Assoc., 1988Google Scholar
Beaton DE, Hogg-Johnson S, Bombardier C. Evaluating changes in health status: reliability and responsiveness of five generic health status measures in workers with musculoskeletal disorders. J Clin Epidemiol 1997; 50: 79–93PubMedCrossRefGoogle Scholar
Matchar DB, Samsa GP, Cohen SJ, et al. A practice improvement trial implemented within managed care organizations: rationale and design of the Managing Anticoagulation Services Trial (MAST). Med Care. In pressGoogle Scholar
Ware JE, Kosinski M, Bayliss MS, et al. Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the medical outcomes study. Med Care 1995; 33 (4): AS264–79Google Scholar
Ware JE, Kosinski M, Keller SD. SF-12: how to score the SF-12 physical and mental health summary scales. Boston (MA): The Health Institute New England Medical Center, 1995Google Scholar
Ware JE, Snow KK, Kosinski M, et al. SF-36 health survey manual and interpretation guide. Boston (MA): The Health Institute New England Medical Center, 1993Google Scholar
McHorney CA, Ware JE, Raczek AE. The MOS 36-item shortform health survey (SF-36): psychometric and clinical tests of validity in measuring physical and mental health constructs. Med Care 1993; 31: 247–63PubMedCrossRefGoogle Scholar
Johnson PA, Goldman L, Orav EJ, et al. Comparison of the medical outcomes study short-form 36-item health survey in black patients and white patients with acute chest pain. Med Care 1995; 33: 145–60PubMedGoogle Scholar
Bergner M, Bobbitt RA, Carter WB, et al. The sickness impact profile: development and final revision of a health status measure. Med Care 1981; 19: 787–805PubMedCrossRefGoogle Scholar
Deyo RA, Diehr P, Patrick DL. Health status measures: statistics and strategies for evaluation. Control Clin Trials 1991; 12 Suppl. 4: 142S–58PubMedCrossRefGoogle Scholar
Fryback DG, Dasbach EJ, Klein R, et al. The beaver dam health outcomes study: initial catalog of health-state quality factors. Med Decis Making 1993; 13: 89–102PubMedCrossRefGoogle Scholar
Kaplan RM, Bush JW, Berry CC. Health status: types of validity and the index of well-being. Health Serv Res 1976; 11: 478–507PubMedGoogle Scholar
Stucki G, Liang MH, Fossel AH, et al. Relative responsiveness of condition-specific and generic health status measures in degenerative spinal stenosis. J Clin Epidemiol 1997; 48: 1369–78CrossRefGoogle Scholar
Damiano AM, Steinberg EP, Cassard SD, et al. Comparison of generic versus disease-specific measures of functional impairment in patients with cataract. Med Care 1995; 33 (4): AS120–30Google Scholar
Weinberger M, Kirkman MS, Samsa GP, et al. The relationship between glycemic control and health-related quality of life in patients with non-insulin-dependent diabetes mellitus. Med Care 1994; 32: 1173–81PubMedCrossRefGoogle Scholar
Bergner M, Hudson LD, Conrad DA, et al. The cost and efficacy of home care for patients with chronic lung disease. Med Care 1988; 26: 566–79PubMedCrossRefGoogle Scholar
Deyo RA, Diehl AK, Rosenthal M. How many days of bed rest for acute low back pain? A randomized clinical trial. N Engl J Med 1986; 315: 1064–70PubMedCrossRefGoogle Scholar
Grotta J, for the US and Canadian Lubeluzole Ischemic Stroke Study Group. Lubeluzole treatment of acute ischemic stroke. Stroke 1997; 28: 2338–46PubMedCrossRefGoogle Scholar
Parmigiani G, Samsa GP, Ancukiewicz M, et al. Assessing uncertainty in cost-effectiveness analysis: application to a complex decision model. Med Decis Making 1997; 17: 390–401PubMedCrossRefGoogle Scholar
Weinstein MC, Stason WB. Hypertension: a policy perspective. Cambridge (MA): Harvard University Press, 1976Google Scholar
Guyatt GH, Juniper EL, Walter SD, et al. Interpreting treatment effects in randomised trials. BMJ 1998; 316: 690–3PubMedCrossRefGoogle Scholar
McHorney CA. Generic health measurement: past accomplishments and a measurement paradigm for the 21st century. Ann Intern Med 1997; 127: 743–50PubMedGoogle Scholar
Revicki DA, Allen H, Bungay K, et al. Responsiveness and calibration of the general well-being adjustment scale in patients with hypertension. J Clin Epidemiol 1994; 47: 1333–42PubMedCrossRefGoogle Scholar