Severity-Stratified Discrete Choice Experiment Designs for Health State Evaluations

Discrete Choice Experiments in Health State Valuation: A Systematic Review of Progress and New Trends

BACKGROUND

Discrete choice experiments (DCEs) are increasingly used in health state valuation studies.

OBJECTIVE

This systematic review updates the progress and new findings of DCE studies in the health state valuation, covering the period since the review of June 2018 to November 2022. The review reports the methods that are currently being used in DCE studies to value health and study design characteristics, and, for the first time, reviews DCE health state valuation studies published in the Chinese language.

METHODS

English language databases PubMed and Cochrane, and Chinese language databases Wanfang and CNKI were searched using the self-developed search terms. Health state valuation or methodology study papers were included if the study used DCE data to generate a value set for a preference-based measure. Key information extracted included DCE study design strategies applied, methods for anchoring the latent coefficient on to a 0-1 QALY scale and data analysis methods.

RESULTS

Sixty-five studies were included; one Chinese language publication and 64 English language publications. The number of health state valuation studies using DCE has rapidly increased in recent years and these have been conducted in more countries than prior to 2018. Wide usage of DCE with duration attributes, D-efficient design and models accounting for heterogeneity has continued in recent years. Although more methodological consensus has been found than in studies conducted prior to 2018, this consensus may be driven by valuation studies for common measures with an international protocol (the 'model' valuation research). Valuing long measures with well-being attributes attracted attention and more realistic design strategies (e.g., inconstant time preference, efficient design and implausible states design) were identified. However, more qualitative and quantitative methodology study is still necessary to evaluate the effect of those new methods.

CONCLUSIONS

The use of DCEs in health state valuation continues to grow dramatically and the methodology progress makes the method more reliable and pragmatic. However, study design is driven by international protocols and method selection is not always justified. There is no gold standard for DCE design, presentation format or anchoring method. More qualitative and quantitative methodology study is recommended to evaluate the effect of new methods before researchers make methodology decisions.

Interaction Effects in Health State Valuation Studies: An Optimal Scaling Approach

OBJECTIVES

This study aimed to introduce a parsimonious modeling approach that enables the estimation of interaction effects in health state valuation studies.

METHODS

Instead of supplementing a main-effects model with interactions between each and every level, a more parsimonious optimal scaling approach is proposed. This approach is based on the mapping of health state levels onto domain-specific continuous scales. The attractiveness of health states is then determined by the importance-weighted optimal scales (ie, main effects) and the interactions between these domain-specific scales (ie, interaction effects). The number of interaction terms only depends on the number of health domains. Therefore, interactions between dimensions can be included with only a few additional parameters. The proposed models with and without interactions are fitted on 3 valuation data sets from 2 different countries, that is, a Dutch latent-scale discrete choice experiment (DCE) data set with 3699 respondents, an Australian time trade-off data set with 400 respondents, and a Dutch DCE with duration data set with 788 respondents.

RESULTS

Important interactions between health domains were found in all 3 applications. The results confirm that the accumulation of health problems within health states has a decreasing marginal effect on health state values. A similar effect is obtained when so-called N3 or N5 terms are included in the model specification, but the inclusion of 2-way interactions provides superior model fits.

CONCLUSIONS

The proposed interaction model is parsimonious, produces estimates that are straightforward to interpret, and accommodates the estimation of interaction effects in health state valuation studies with realistic sample size requirements. Not accounting for interactions is shown to result in biased value sets, particularly in stand-alone DCE with duration studies.

The Danish EQ-5D-5L Value Set: A Hybrid Model Using cTTO and DCE Data

OBJECTIVES

Quality-adjusted life-years (QALYs) are expected to be used for priority setting of hospital-dispensed medicines in Denmark from 2021. The aim of this study was to develop the first Danish value set for the EQ-5D-5L based on interviews with a representative sample of the Danish adult population.

METHODS

A nationally representative sample based on age (> 18 years), gender, education, and geographical region was recruited using data provided by Statistics Denmark. Computer-assisted personal interviews were carried out using the EQ-VT 2.1. Respondents each valued ten health states using composite time trade-off (cTTO) and seven health states using discrete-choice experiment (DCE). Different predictive models were explored using cTTO and DCE data alone or in combination as hybrid models. Model performance was assessed using logical consistency.

RESULTS

A total of 1014 interviews were included in the analyses. The sample was representative of the Danish adult population, though the sample contained slightly more respondents with higher education than in the general population. Only the heteroscedastic censored hybrid model combining cTTO and DCE data yielded consistent results, and hence was chosen for modelling the final Danish value set. The predicted values ranged from - 0.757 to 1, and anxiety/depression was the dimension assigned most value by respondents.

CONCLUSIONS

This study established the Danish EQ-5D-5L value set, which represents the preferences of the Danish general population, and is expected to provide key input for healthcare decision-making in a Danish context.

Effects of Metreleptin on Patient Outcomes and Quality of Life in Generalized and Partial Lipodystrophy

Generalized and partial lipodystrophy are rare and complex diseases with progressive clinical and humanistic burdens stemming from selective absence of subcutaneous adipose tissue, which causes reduced energy storage capacity and a deficiency of adipokines such as leptin. Treatment options were limited before leptin replacement therapy (metreleptin) became available. This retrospective study evaluates both clinical and humanistic consequences of the disease and treatment. Chart data were abstracted from a cohort of metreleptin-treated patients with generalized and partial lipodystrophy (n = 112) treated at the US National Institutes of Health. To quantify the quality-of-life consequences of the lipodystrophy disease attributes recorded in chart data, a discrete choice experiment was completed in 6 countries (US, n = 250; EU, n = 750). Resulting utility decrements were used to estimate the quality-adjusted life-year consequences of changes in lipodystrophy attribute prevalence before and after metreleptin. In addition to metabolic impairment, patients with generalized and partial lipodystrophy experienced a range of lipodystrophy consequences, including liver abnormality (94%), hyperphagia (79%), impaired physical appearance (77%), kidney abnormality (63%), reproductive dysfunction (80% of females of reproductive age), and pancreatitis (39%). Improvement was observed in these attributes following initiation of metreleptin. Quality-adjusted life-year gains associated with 12 months of treatment with metreleptin were estimated at 0.313 for generalized and 0.117 for partial lipodystrophy, reducing the gap in quality of life between untreated lipodystrophy and perfect health by approximately 59% and 31%, respectively. This study demonstrates that metreleptin is associated with meaningful clinical and quality-of-life improvements.

Implausible States: Prevalence of EQ-5D-5L States in the General Population and Its Effect on Health State Valuation

The EQ-5D is made up of health state dimensions and levels, in which some combinations seem less "plausible" than others. If "implausible" states are used in health state valuation exercises, then respondents may have difficulty imagining them, causing measurement error. There is currently no standard solution: some valuation studies exclude such states, whereas others leave them in. This study aims to address 2 gaps in the literature: 1) to propose an evidence-based set of the least prevalent two-way combinations of EQ-5D-5L dimension levels and 2) to quantify the impact of removing perceived implausible states from valuation designs. For the first aim, we use data from 2 waves of the English General Practitioner Patient Survey (n = 1,639,453). For the second aim, we remodel a secondary data set of a Discrete Choice Experiment (DCE) with duration that valued EQ-5D-5L and compare across models that drop observations involving different health states: 1) implausible states as defined in the literature, 2) the least prevalent states identified in stage 1, and 3) randomly select states, alongside 4) a model that does not drop any observations. The results indicate that two-way combinations previously thought to be implausible actually exist among the general population; there are other combinations that are rarer, and removing implausible states from an experimental design of a DCE with duration leads to value sets with potentially different characteristics depending on the criterion of implausible states. We advise against the routine removal of implausible states from health state valuation studies.

On the Optimization of Bayesian D-Efficient Discrete Choice Experiment Designs for the Estimation of QALY Tariffs That Are Corrected for Nonlinear Time Preferences

OBJECTIVES

This article explains how to optimize Bayesian D-efficient discrete choice experiment (DCE) designs for the estimation of quality-adjusted life year (QALY) tariffs that are unconfounded by respondents' time preferences.

METHODS

The calculation of Bayesian D-errors is explained for DCE designs that allow for the disentanglement of respondents' time and health-state preferences. Time preferences are modelled via an exponential, hyperbolic, or power discount function and the performance of the proposed DCE designs is compared with that of several conventional DCE designs that do not take nonlinear time preferences into account.

RESULTS

Based on the achieved D-error, asymptotic standard error, and estimated sample size to obtain statistically significant estimates of the discount rate parameters, the proposed designs outperform the conventional DCE designs.

CONCLUSIONS

We recommend that applied researchers use appropriately optimized DCE designs for the estimation of QALY tariffs that are corrected for time preferences. The TPC-QALY software package that accompanies this article makes the recommended designs easily accessible for health-state valuation researchers.

Overview, Update, and Lessons Learned From the International EQ-5D-5L Valuation Work: Version 2 of the EQ-5D-5L Valuation Protocol

A standardized 5-level EuroQol 5-dimensional questionnaire (EQ-5D-5L) valuation protocol was first used in national studies in the period 2012 to 2013. A set of problems encountered in this initial wave of valuation studies led to the subsequent refinement of the valuation protocol. To clarify lessons learned and how the protocol was updated when moving from version 1.0 to the current version 2.1 and 2.0, this article will (1) present the challenges faced in EQ-5D-5L valuation since 2012 and how these were resolved and (2) describe in depth a set of new challenges that have become central in currently ongoing research on how EQ-5D-5L health states should be valued and modeled.