The precision of health state valuation by members of the general public using the standard gamble
- 212 Downloads
Precision is a recognised requirement of patient-reported outcome measures but no previous studies of the precision of methods for obtaining health state values from the general public, based on specific health state descriptions or vignettes, have been carried out. The methodological requirements of policy makers internationally is driving growth in the use of methods to obtain utilities from the general public to inform cost per quality-adjusted life-year (QALY) analyses of health technologies being considered for adoption by health systems.
The precision of five comparisons of the outcomes of treatments, based on health state descriptions, was assessed against the results of clinical trials which showed a statistically and clinically significant improvement using an internet panel of members of the UK general public. Health states were developed to depict the baseline and post-treatment states from these exemplar clinical trials. Preferences for health states were obtained using bottom-up titrated standard gamble over the internet, and differences between summary health state values corresponding to the treatment and comparator groups within each exemplar study were compared. Results are considered in the context of various estimates for the minimally important difference in utility values.
Participation among members of the internet panel in the five exemplars ranged from 27 to 59. In four of the five exemplars, the utility-based estimates of treatment benefit showed significant differences between groups and were greater than an assumed minimally important difference of 0.1. Mean utility differences between groups were: 0.23 (computerised cognitive behavioural therapy for depression, P < 0.001), 0.11 (hip resurfacing for hip osteoarthritis, P < 0.001), 0.0005 (cognitive behavioural therapy for insomnia, P = 0.98), 0.15 (pulmonary rehabilitation for COPD, P < 0.001) and 0.11 (infliximab for Crohn’s disease, P < 0.001). The confidence intervals around the estimates of utility-based treatment effect in three of the five examples did not exclude the possibility of a difference smaller than a minimally important difference of 0.1. Recent empirical evidence suggests a lower minimally important difference (0.03) may be more appropriate, in which case our results provide further reassurance of preservation of precision in health state description and valuation.
The precision of estimates of treatment effects based on preference data obtained from disease-specific measurements in clinically significant studies of health technologies was acceptable using an internet-based panel of members of the general public and the standard gamble. Definition of the minimally important difference in utility estimates is required to adequately assess precision and should be the subject of further research.
KeywordsUtility Preferences Internet Public Precision
NHS R&D Programme; National Institute for Health and Clinical Excellence (NICE); NHS Quality Improvement Scotland (NHSQIS). We are extremely grateful to the following for their help: the members of the internet panel, the patients and clinicians who provided help in the development of health state descriptions, Joanne Perry for her project support, Dan Fall (University of Sheffield) and Stephen Elliott (Llama Digital) for website development.
K.S., R.M., J.B. and A.R. conceived the study and, with J.R., designed the evaluation. M.D. developed some of the health state descriptions and contributed to data collection. All authors contributed to the drafting of this report.
- 1.Fitzpatrick, R., Davey, C., Buxton, M., & Jones, D. (1998). Evaluating patient based outcome measures for use in clinical trials. Health Technology Assessment, 2(14), i–iv.Google Scholar
- 3.Stewart, A. L. (1992). Conceptual and methodologic issues in defining quality of life: State of the art. Progress in Cardiovascular Nursing, 7, 3–11.Google Scholar
- 7.Stein, K., Dyer, M., Crabb, T., Milne, R., Round, A., Ratcliffe, J., & Brazier, J. (2006). An internet “value of health” panel: Recruitment, participation and compliance. Health and Quality of Life Outcomes, 4, 90.Google Scholar
- 12.Brazier, J., & Dolan, P. (2005). Evidence of preference construction in a comparison of variants of the standard gamble method. Health Economics and Decision Science Section Discussion Papers, University of Sheffield.Google Scholar
- 13.Lenert, L. A., & Sturley, A. E. (2002). Use of the internet to study the utility values of the public. In AMIA annual symposium proceedings (pp. 440–444).Google Scholar
- 17.Vale, L., Wyness, L., McCormack, K., McKenzie, L., Brazelli, M., & Stearns, S. (2001). Systematic review of the effectiveness and cost effectiveness of metal on metal hip resurfacing for treatment of hip disease. Health Services Research Unit, University of Aberdeen.Google Scholar
- 18.Morgan, K., Dixon, S., Mathers, N., Thompson, J., & Tomeny, M. (2004). Psychological treatment for insomnia in the regulation of long-term hypnotic drug use. Health Technology Assessment, 8, 1–94.Google Scholar
- 20.Man, W. D., Polkey, M. I., Donaldson, N., Gray, B. J., & Moxham, J. (2004). Community pulmonary rehabilitation after hospitalisation for acute exacerbations of chronic obstructive pulmonary disease: Randomised controlled study. BMJ (Clinical Research Ed.), 329, 1209. doi: 10.1136/bmj.38258.662720.3A.CrossRefGoogle Scholar
- 21.Lacasse, Y., Wong, E., & Guyatt, G. (1974). A systematic overview of the measurement properties of the chronic respiratory questionnaire. Canadian Respiratory Journal, 4, 131–139.Google Scholar
- 24.Stein, K., & Milne, R. (1998). Health technology assessment. In M. Baker & S. Kirk (Eds.), Research and development in the NHS. Oxford: Radcliffe Medical.Google Scholar
- 25.Williams, A. (1995). The measurement and valuation of health: A chronicle. University of York, York.Google Scholar
- 26.Garside, R., Stein, K., Castelnuovo, E., Pitt, M., Aschcroft, D., Dimmock, P., et al. (2005). The effectiveness and cost-effectiveness of pimecrolimus and tacrolimus for atopic eczema: A systematic review and economic evaluation. Health Technology Assessment, 9, 1–264.Google Scholar
- 29.Brozek, J., Guyatt, G. H., & Schunemann, H. (2006). How a well-grounded minimal important difference can enhance transparency of labelling claims and improve interpretation of a patient reported outcome measure. Health and Quality of Life Outcomes, 4, 69.Google Scholar