Abstract
To appropriately weight dimensions of quality of life instruments for health economic evaluations, population and patient preferences need to be elicited. Two commonly used elicitation methods for this purpose are discrete choice experiments (DCE) and case 2 best-worst scaling (BWS). These methods differ in terms of their cognitive burden, which is especially relevant when eliciting preferences among older people. Using a randomised experiment with respondents from an online panel, this paper examines the cognitive burden associated with colour-coded and level overlapped DCE, colour-coded BWS, and ‘standard’ BWS choice tasks in a complex health state valuation setting. Our sample included 469 individuals aged 65 and above. Based on both revealed and stated cognitive burden, we found that the DCE tasks were less cognitively burdensome than case 2 BWS. Colour coding case 2 BWS cannot be recommended as its effect on cognitive burden was less clear and the colour coding lead to undesired choice heuristics. Our results have implications for future health state valuations of complex quality of life instruments and at least serve as an example of assessing cognitive burden associated with different types of choice experiments.
Highlights
Developments like ageing populations and rapid advances in medical technology create challenges for budgets of publicly funded health care systems (de Meijer et al, 2013)
The profiles shown to respondents in both discrete choice experiments (DCE) and best-worst scaling (BWS) tasks corresponded to well-being states, described using the nine dimensions of the Well-being of Older People instrument (WOOP).4
Our study contributes to the literature by providing empirical evidence on 1) whether DCE or BWS choice tasks are associated with lower cognitive burden in the context of health or well-being state valuation in an older population sample, and 2) whether colour coding of BWS tasks affects cognitive burden and to a lesser extent validity of BWS experiments
Summary
Developments like ageing populations and rapid advances in medical technology create challenges for budgets of publicly funded health care systems (de Meijer et al, 2013). Health technology assessment (HTA) generates valuable insights to support this decision-making process, using tools like cost-utility analysis. There, the benefits of health technologies are typically expressed in the incremental amount of health changes they produce. This is calculated based on data from generic, multidimensional quality of life instruments, and a weighting algorithm for the levels of the dimensions based on population or patient preferences (Neumann et al, 2016).
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