Mapping between EQ-5D-5L and EQ-5D-3L using the NICE Decision Support Unit models

The NICE Decision Support Unit (DSU) provides models that enable mapping between EQ-5D-5L and EQ-5D-3L data. These models can be applied to health states, utility index scores and summarised utility values, allowing results obtained using different EQ-5D descriptive systems to be compared on a common basis.

The DSU models require age and sex in addition to EQ-5D responses and support mapping in both directions between EQ-5D-3L and EQ-5D-5L. In the UK, prior to the release of the 2026 EQ-5D-5L UK value set, NICE recommended the use of these models for reference-case analyses involving mapping between EQ-5D versions. The DSU models remain useful for mapping between EQ-5D versions and for the analysis of studies conducted under earlier NICE guidance.

This vignette demonstrates how the DSU models can be used from within the eq5d package.

Available value sets

DSU value sets available in eq5d can be viewed using the valuesets() function. Results can be filtered by EQ-5D version, country or value set type.

  library(eq5d)

## Loading required package: lifecycle

## Loading required package: rlang

  # DSU value sets for the UK
  valuesets(version = "5L", type = "DSU", country = "UK")

##    Version Type Country   PubMed                        DOI ISBN
## 1 EQ-5D-5L  DSU UK_2026 36449173 10.1007/s40273-022-01218-7 <NA>
##                                                                ExternalURL
## 1 https://sheffield.ac.uk/nice-dsu/methods-development/mapping-eq-5d-5l-3l

  ## All DSU EQ-5D-5L to EQ-5D-3L value sets
  head(valuesets(version = "5L", type = "DSU"))

##    Version Type      Country   PubMed                        DOI ISBN
## 1 EQ-5D-5L  DSU    Australia       NA                       <NA> <NA>
## 2 EQ-5D-5L  DSU      Belgium       NA                       <NA> <NA>
## 3 EQ-5D-5L  DSU       Canada       NA                       <NA> <NA>
## 4 EQ-5D-5L  DSU        China       NA                       <NA> <NA>
## 5 EQ-5D-5L  DSU      Denmark       NA                       <NA> <NA>
## 6 EQ-5D-5L  DSU England_2018 36449173 10.1007/s40273-022-01218-7 <NA>
##                                                                ExternalURL
## 1 https://sheffield.ac.uk/nice-dsu/methods-development/mapping-eq-5d-5l-3l
## 2 https://sheffield.ac.uk/nice-dsu/methods-development/mapping-eq-5d-5l-3l
## 3 https://sheffield.ac.uk/nice-dsu/methods-development/mapping-eq-5d-5l-3l
## 4 https://sheffield.ac.uk/nice-dsu/methods-development/mapping-eq-5d-5l-3l
## 5 https://sheffield.ac.uk/nice-dsu/methods-development/mapping-eq-5d-5l-3l
## 6 https://sheffield.ac.uk/nice-dsu/methods-development/mapping-eq-5d-5l-3l

Mapping health states

Health states can be mapped using either individual EQ-5D dimensions or five-digit health-state codes. In addition to EQ-5D responses, age and sex must be supplied.

Age may be provided either as years or as an age category. Age categories range from 1 to 5, where 1 = 18-34, 2 = 35-44, 3 = 45-54, 4 = 55-64 and 5 = 65-100. Equivalent age values and age categories produce the same results (for example, age 47 and age category 3). Sex may be specified as “Male”, “Female”, “M”, or “F” and matching is case-insensitive.

Single health states

# Using age in years and sex as "m"
eq5d(c(MO=1, SC=2, UA=3, PD=4, AD=5), type = "DSU", country = "England_2018", age = 43, version = "5L", sex = "m")

## [1] 0.065

# Using an age category and sex as "Male"
eq5d(12345, type = "DSU", country = "England_2018", age = 2, version = "5L", sex = "Male")

## [1] 0.065

Multiple health states

# get states and create data.frame
set.seed(12345)
dat1 <- data.frame(State = sample(get_all_health_states("5L"), 10), 
                   Age = sample(18:100, 10), 
                   Sex = sample(c("M","F"), replace = TRUE, 10))

print(dat1)

##    State Age Sex
## 1  43335  19   F
## 2  11311  92   F
## 3  21445  55   F
## 4  21515  27   F
## 5  25544  49   F
## 6  52432  57   M
## 7  22411  56   M
## 8  15515  98   F
## 9  52125  47   M
## 10 44134  18   F

eq5d(dat1, version="5L", type="DSU", country="England_2018")

##  [1]  0.081  0.894  0.026  0.260 -0.137  0.360  0.684 -0.097  0.186  0.155

Mapping utility scores

Utility scores can also be mapped between EQ-5D versions. Exact utility values are supplied in place of health states.

# Using utility score 0.322 (score for state 12345), an age category and sex as "Male"
eq5d(0.322, type = "DSU", country = "England_2018", age = 2, version = "5L", sex = "Male")

## [1] 0.235

# Multiple states

# create data.frame of utility scores using the 2018 EQ-5D-5L value set for England for the states in dat1
dat2 <- data.frame(Utility = eq5d(dat1$State, version = "5L", type = "VT", country = "England"), 
                   Age = dat1$Age,
                   Sex = dat1$Sex)
                   
print(dat2)

##    Utility Age Sex
## 1    0.277  19   F
## 2    0.937  92   F
## 3    0.215  55   F
## 4    0.469  27   F
## 5   -0.006  49   F
## 6    0.352  57   M
## 7    0.730  56   M
## 8    0.324  98   F
## 9    0.324  47   M
## 10   0.260  18   F

eq5d(dat2, version="5L", type="DSU", country="England_2018")

##  [1]  0.127  0.880  0.026  0.283 -0.138  0.318  0.684  0.130  0.169  0.155

Mapping summarised utility scores

If approximate or summarised utility scores are being mapped, a bandwidth parameter needs to be provided in addition to the age and sex parameters. The bandwidth parameter specifies the neighbourhood and the rate at which the weight declines with distance. It is possible to provide a single bandwidth score that can be applied to all utility scores in a dataset. The DSU recommend bandwidth values of 0.2 for utilities below 0.8, 0.1 for utilities between 0.8 and 0.951, and a small bandwidth sufficient to include 1.0 for utilities above 0.951. Individual bandwidth values can also be supplied using a bwidth column. For more information please view the tutorial on the NICE DSU website.

# Get all utility scores from 2018 EQ‑5D‑5L value set for England 
exist.utils <- unique(DSU5L$England_2018)
min <- min(DSU5L$England_2018)
max <- max(DSU5L$England_2018)

# calculate range of values between min and max
poss.utils <- seq(from=min, to=max, by=0.001)

# create data.frame of 10 utility scores that aren't in the 2018 EQ‑5D‑5L value set for England 
set.seed(54321)
dat3 <- data.frame(Utility = sample(poss.utils[which(!poss.utils %in% exist.utils)], 10),
                   Age = sample(18:100, 10), 
                   Sex = sample(c("M","F"), replace = TRUE, 10))

print(dat3)

##    Utility Age Sex
## 1    0.685  84   F
## 2    0.876  68   F
## 3    0.738  72   F
## 4    0.373  70   F
## 5    0.904  80   M
## 6    0.666  49   F
## 7    0.098  90   F
## 8    0.418  73   F
## 9    0.701  99   F
## 10   0.997  91   F

# map scores using a single bandwidth value for all scores
eq5d(dat3, version="5L", type="DSU", country="England_2018", bwidth=0.2)

##  [1]  0.563  0.717  0.616  0.203  0.736  0.541 -0.071  0.253  0.580  0.801

# add bwidth column with values based on the DSU recommendations
dat3$bwidth <- c(0.2, 0.1, 0.2, 0.2, 0.1, 0.2, 0.2, 0.2, 0.2, 0.01)

eq5d(dat3, version="5L", type="DSU", country="England_2018")

##  [1]  0.563  0.764  0.616  0.203  0.792  0.541 -0.071  0.253  0.580  0.988

Differences can be observed in the 2nd, 5th, and 10th mapped scores when the DSU recommended bandwidth values are used instead of a single bandwidth value of 0.2 for all utility scores.