1000minds supports the EuroQol Group's EQ-5D-5L and EQ-5D-3L in these two special ways.
A tool for creating value sets, fast!
A specialised 1000minds system is available for creating both personal and social EQ-5D-5L and EQ-5D-3L value sets – i.e. 3125 (55) and 243 (35) values respectively. These value sets are created from 1000minds' discrete choice experiments (DCEs) based on our PAPRIKA method and a modified binary search algorithm for finding any health states worse than dead (News »).
Potentially 1000s of people can be surveyed to obtain a representative sample of a country's population. Compared to other approaches, the tool significantly reduces the cost and time involved in creating and analyzing value sets.
Want to experience the tool yourself and generate your own EQ-5D-5L value set (all 3125 values)?
This demonstration version of the tool includes just the DCE and the binary search algorithm, and is intended for a ‘knowledgeable’ audience (you!). The general population tool has more user-friendly instructions and finishes with questions about participants' demographic characteristics, etc.
Use of the tool in New Zealand
T Sullivan, P Hansen, F Ombler, S Derrett & N Devlin, “A new tool for creating personal and social EQ-5D-5L value sets, including valuing ‘dead’”, Economics Discussion Paper No 1903, University of Otago
How significant are correlations between value sets?
As explained in the recently published article below, high correlation coefficients for EQ-5D value sets derived from different samples – across countries and/or using different valuation techniques – are conventionally interpreted as evidence that the people in the respective samples have similar health-related quality of life (HRQoL) preferences. However, EQ-5D-3L and EQ-5D-5L value sets contain many inherent rankings of health state values by design.
By calculating correlation coefficients for value sets created from random data, we demonstrate in the article that 'high’ coefficients are artifacts of these inherent rankings – e.g. median Pearson’s r = 0.783 for the EQ-5D-3L and 0.850 for the EQ-5D-5L instead of zero. Therefore, high correlation coefficients do not necessarily constitute evidence of meaningful associations in terms of similar HRQoL preferences.
Based on simulations (explained in the article), we calculate significance levels and find that many high coefficients reported in the literature are not statistically significant. These 'high' but insignificant correlations are in fact spurious.
Here is our tool to lookup the statistical significance of correlation coefficients for EQ-5D-3L and EQ-5D-5L value sets:
This article explains the method for estimating the significance levels reported in the tool:
F Ombler, M Albert & P Hansen (2018), “How significant are ‘high’ correlations between EQ-5D value sets?”, Medical Decision Making, early online version.