An Australian Discrete Choice Experiment to Value Eq‐5d Health States

Abstract

Conventionally, generic quality-of-life health states, defined within multi-attribute utility instruments, have been valued using a Standard Gamble or a Time Trade-Off. Both are grounded in expected utility theory but impose strong assumptions about the form of the utility function. Preference elicitation tasks for both are complicated, limiting the number of health states that each respondent can value and, therefore, that can be valued overall. The usual approach has been to value a set of the possible health states and impute values for the remainder. Discrete Choice Experiments (DCEs) offer an attractive alternative, allowing investigation of more flexible specifications of the utility function and greater coverage of the response surface. We designed a DCE to obtain values for EQ-5D health states and implemented it in an Australia-representative online panel (n = 1,031). A range of specifications investigating non-linear preferences with respect to time and interactions between EQ-5D levels were estimated using a random-effects probit model. The results provide empirical support for a flexible utility function, including at least some two-factor interactions. We then constructed a preference index such that full health and death were valued at 1 and 0, respectively, to provide a DCE-based algorithm for Australian cost-utility analyses.

Using Discrete Choice Experiments with Duration to Model EQ-5D-5L Health State Preferences

“…Other studies in this area have used similar numbers of choice sets and produced logically consistent models. [6][7][8]11 Regarding aim 2, the model estimated on the non-zero prior value design data was more inconsistent than the model estimated using the zero prior value data, with less precise coefficient estimates. To our knowledge this is the first comparison of zero and non-zero prior value designs using DCE TTO , and the findings are unexpected, since the introduction of the non-zero prior values should, in theory, improve the precision of the parameter estimates.…”

“…[6][7][8]11 It is possible that using more levels will increase the validity of responses as it enables for smaller differences between life years in choice sets. Using more levels of duration that have closer ratios may make it less likely that respondents will choose on the basis of duration alone (i.e.…”

Testing a discrete choice experiment including duration to value health states for large descriptive systems: Addressing design and sampling issues

“…This is analogous to Norman et al. (2013) and Viney et al. (2014) and can be solved as:$V_j=1+\frac{{\hat{\lambda }}^{\prime }}{\hat{\beta }}{\mathrm{bold-italicx}}_j$…”

Treatment and Outcomes of Working Aged Adults with Stroke: Results from a National Prospective Registry

“…Responses to the EQ-5D-3L were converted to index-based values (“utilities”) using health values derived with Discrete Choice Experiment methods in Australia [21]. Utility scores range from –1 to 1 with negative utility scores indicating a state worse than death, a utility score of 0 corresponding to a state equivalent to death, and a score of 1 representing perfect HRQoL.…”