Valuing SF-6D Health States Using a Discrete Choice Experiment

Cost-effectiveness analysis alongside clinical trials II-An ISPOR Good Research Practices Task Force report

Clinical trials evaluating medicines, medical devices, and procedures now commonly assess the economic value of these interventions. The growing number of prospective clinical/economic trials reflects both widespread interest in economic information for new technologies and the regulatory and reimbursement requirements of many countries that now consider evidence of economic value along with clinical efficacy. As decision makers increasingly demand evidence of economic value for health care interventions, conducting high-quality economic analyses alongside clinical studies is desirable because they broaden the scope of information available on a particular intervention, and can efficiently provide timely information with high internal and, when designed and analyzed properly, reasonable external validity. In 2005, ISPOR published the Good Research Practices for Cost-Effectiveness Analysis Alongside Clinical Trials: The ISPOR RCT-CEA Task Force report. ISPOR initiated an update of the report in 2014 to include the methodological developments over the last 9 years. This report provides updated recommendations reflecting advances in several areas related to trial design, selecting data elements, database design and management, analysis, and reporting of results. Task force members note that trials should be designed to evaluate effectiveness (rather than efficacy) when possible, should include clinical outcome measures, and should obtain health resource use and health state utilities directly from study subjects. Collection of economic data should be fully integrated into the study. An incremental analysis should be conducted with an intention-to-treat approach, complemented by relevant subgroup analyses. Uncertainty should be characterized. Articles should adhere to established standards for reporting results of cost-effectiveness analyses. Economic studies alongside trials are complementary to other evaluations (e.g., modeling studies) as information for decision makers who consider evidence of economic value along with clinical efficacy when making resource allocation decisions.

A comparison of methods for converting DCE values onto the full health-dead QALY scale

BACKGROUND

Preference elicitation techniques such as time trade-off (TTO) and standard gamble (SG) receive criticism for their complexity and difficulties of use. Ordinal techniques such as discrete choice experiment (DCE) are arguably easier to understand but generate values that are not anchored onto the full health-dead 1-0 quality-adjusted life-year (QALY) scale required for use in economic evaluation.

METHODS

This article compares existing methods for converting modeled DCE latent values onto the full health-dead QALY scale: 1) anchoring DCE values using dead as valued in the DCE and 2) anchoring DCE values using TTO value for worst state to 2 new methods: 3) mapping DCE values onto TTO and 4) combining DCE and TTO data in a hybrid model. Models are compared using their ability to predict mean TTO health state values.

DATA

We use postal DCE data (n = 263) and TTO data (n = 307) collected by interview in a general population valuation study of an asthma condition-specific measure (AQL-5D).

RESULTS

New methods 3 and 4 using mapping and hybrid models are better able to predict mean TTO health state values (mean absolute difference [MAD], 0.052-0.084) than the anchor-based methods (MAD, 0.075-0.093) and were better able to predict mean TTO health state values even when using in their estimation a subsample of the available TTO data.

CONCLUSIONS

These new mapping and hybrid methods have a potentially useful role for producing values on the QALY scale from data elicited using ordinal techniques such as DCE for use in economic evaluation that makes best use of the desirable properties of each elicitation technique and elicited data. Further research is encouraged.

A framework for estimating health state utility values within a discrete choice experiment: modeling risky choices

BACKGROUND

There has been recent interest in using the discrete choice experiment (DCE) method to derive health state utilities for use in quality-adjusted life year (QALY) calculations, but challenges remain.

OBJECTIVES

We set out to develop a risk-based DCE approach to derive utility values for health states that allowed 1) utility values to be anchored directly to normal health and death and 2) worse than dead health states to be assessed in the same manner as better than dead states. Furthermore, we set out to estimate alternative models of risky choice within a DCE model.

METHOD

A survey was designed that incorporated a risk-based DCE and a "modified" standard gamble (SG). Health state utility values were elicited for 3 EQ-5D health states assuming "standard" expected utility (EU) preferences. The DCE model was then generalized to allow for rank-dependent expected utility (RDU) preferences, thereby allowing for probability weighting. A convenience sample of 60 students was recruited and data collected in small groups.

RESULTS

Under the assumption of "standard" EU preferences, the utility values derived within the DCE corresponded fairly closely to the mean results from the modified SG. Under the assumption of RDU preferences, the utility values estimated are somewhat lower than under the assumption of standard EU, suggesting that the latter may be biased upward.

CONCLUSION

Applying the correct model of risky choice is important whether a modified SG or a risk-based DCE is deployed. It is, however, possible to estimate a probability weighting function within a DCE and estimate "unbiased" utility values directly, which is not possible within a modified SG. We conclude by setting out the relative strengths and weaknesses of the 2 approaches in this context.