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Gepubliceerd in: Quality of Life Research 11/2020

23-07-2020

3125 steps to perfect health: a nonparametric approach to developing the EQ-5D-5L value set

Auteurs: Stephanie Thomas, Jeffrey A. Johnson, Feng Xie

Gepubliceerd in: Quality of Life Research | Uitgave 11/2020

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Abstract

Purpose

The EQ-5D-5L is a commonly used instrument for assessing the utility of different health states. Health state utility values are a key component of health technology evaluations. Such evaluations are used to support evidence-based decisions surrounding health resource allocations and therefore rely on the accuracy of the valuation set used. This paper takes an alternative approach to developing an EQ-5D-5L value set for Canada. The aim is to introduce a robust method that is likely to generate a value set with improved accuracy and that can be used to generate value sets for other populations without the need for modification.

Methods

The common approach to developing a valuation set for preference-based instruments is to ask a population sample to value a subset of the health states using an established preference elicitation technique. The relationship between the elicited health states and the preferences is used to inform a model to predict the utility values for the unsampled health states described by the instrument. The true relationship is unknown and the functional forms chosen in the modelling process vary across valuation studies. We use nonparametric local constant regression to estimate an EQ-5D-5L value set for Canada and propose this method as an alternative for value set development because it does not require the specification of a functional form at the outset.

Results

Compared to the existing valuation model for Canada, the nonparametric method improves in-sample fit, reducing the average squared prediction error by 94.46% and the mean absolute error by 79.37%. In four of five sets of out-of-sample studies, this new approach performs significantly better than 9 comparison models. Despite lacking any restriction on the functional form of the resulting valuations, the valuation set generated by this new approach is logically consistent. 100% of the pairs of health states in which one state is dominant have health state values which respect this ordering. The value set also appears to differ substantially from the comparators.

Conclusions

Overall, the results suggest that nonparametric regression is a promising tool for the estimation of EQ-5D-5L valuation sets and may be a good option in a standardised methodology for value set development.
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Voetnoten
1
Other versions include the EQ-5D-3L which offered three levels for each dimension.
 
2
Discrete choice experiment tasks are also included in the standard EQ-VT protocol.
 
3
For example, by estimating a linear model.
 
4
Note that the data set we used is slightly different than the data used in the original publication where traditional time trade-off data were also included in the modelling process.
 
5
Who also, very graciously, provided some of the code used here.
 
6
We also investigated interviewer effects, and, like [4], found these to be relevant but much less informative than individual effects. We thus omit those results from this study.
 
7
For each health state, a prediction for every respondent was generated. The mean of those predictions serves as the valuation.
 
8
The utility value for 11111 was higher than the utility value for 12111 for the pair to be classified as consistent.
 
9
For the 3125 possible health states, there are 4,881,250 possible unique pairings of health states. 756,250 of these pairings exhibit a clear dominance of one state over the other. Each of these dominant pairings was evaluated.
 
10
To adjust for the possibility that some differences between the base state and the transition states may represent more or less than a minimally important difference, we also generated a ‘trimmed’ MID by dropping the lowest and highest differences between the base state and the transition state for each base state, and proceed as before with averaging. These results are presented in Appendix C.4.2.
 
11
See Appendix C.4.2 for full results of the MID procedures
 
12
The accompanying distributions of the differences are illustrated in Appendix Figure 9 for reference.
 
13
The Bland–Altman critical difference is 2 standard deviations above and below the mean of the differences.
 
14
Partial effects surfaces present the results of each variable while keeping all other variables fixed at specific levels so interactions can be seen only by systematically varying the combinations of levels to fix upon.
 
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Metagegevens
Titel
3125 steps to perfect health: a nonparametric approach to developing the EQ-5D-5L value set
Auteurs
Stephanie Thomas
Jeffrey A. Johnson
Feng Xie
Publicatiedatum
23-07-2020
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 11/2020
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-020-02589-0

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