Analysing EQ-5D data"

In eq5dsuite: Handling and Analysing EQ-5d Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width  = 7,
  fig.height = 4
)
library(eq5dsuite)

Introduction

This vignette demonstrates how eq5dsuite supports the full EQ-5D analytical workflow recommended by Devlin et al. (2020), using the NHS Patient Reported Outcome Measures (PROMs) dataset bundled with the package. The dataset includes patients undergoing hip replacement, knee replacement, groin hernia repair, and varicose vein surgery, who completed the EQ-5D-3L before and after their procedure.

# The example dataset is bundled with the package
data(example_data)
head(example_data)

Worked example 1: Hip replacement outcomes

This example illustrates a typical single-group longitudinal workflow, addressing three practical questions: which problems do patients report before surgery, how does health change following the procedure, and how large is the overall health gain?

Data preparation

dim_names <- c("mo", "sc", "ua", "pd", "ad")

# Subset to hip replacement patients
hip_data <- example_data[example_data$procedure == "Hip Replacement", ]

# Add profile code and EQ-5D value
hip_data$profile_code <- toEQ5Dindex(
  x         = hip_data,
  dim.names = dim_names
)
hip_data$value <- eq5d3l(
  hip_data[, dim_names],
  country   = "UK",
  dim.names = dim_names
)

# Pre-operative subset
hip_preop <- hip_data[hip_data$time == "Pre-op", ]

Pre-operative profile distribution

eq5d_profile_level_summary(
  df           = hip_preop,
  names_eq5d   = dim_names,
  eq5d_version = "3L"
)

Most patients report problems with pain/discomfort, usual activities, and mobility, consistent with the profile expected in patients awaiting hip replacement.

Most common health states

eq5d_profile_top_states(
  df           = hip_preop,
  names_eq5d   = dim_names,
  eq5d_version = "3L",
  n            = 5
)

Change between time points: PCHC

The Paretian Classification of Health Change (PCHC) classifies each patient as Better, Worse, Mixed, or No Change between time points. All plot functions in eq5dsuite return ggplot2 objects that can be customised directly:

eq5d_profile_pchc_by_group_plot(
  df         = hip_data,
  name_id    = "id",
  names_eq5d = dim_names,
  name_fu    = "time",
  levels_fu  = c("Pre-op", "Post-op")
)$p +
  ggplot2::labs(
    title = "PCHC: hip replacement patients",
    x     = NULL,
    y     = "Percentage of respondents"
  ) +
  ggplot2::theme_minimal()

Dimensions driving improvement

eq5d_profile_better_dimensions_by_group_plot(
  df         = hip_data,
  name_id    = "id",
  names_eq5d = dim_names,
  name_fu    = "time",
  levels_fu  = c("Pre-op", "Post-op")
)$p +
  ggplot2::theme_minimal()

EQ-5D value summary

eq5d_utility_summary(
  df           = hip_data,
  name_fu      = "time",
  levels_fu    = c("Pre-op", "Post-op"),
  names_eq5d   = dim_names,
  eq5d_version = "3L",
  country      = "UK"
)

EQ-VAS summary

eq5d_vas_summary(
  df        = hip_data,
  name_vas  = "vas",
  name_fu   = "time",
  levels_fu = c("Pre-op", "Post-op")
)

---

Worked example 2: Comparing two procedures

This example demonstrates a cross-sectional group comparison using pre-operative data from knee replacement and groin hernia patients — two groups with very different levels of pre-operative health burden.

Data preparation

# Subset to two procedures, pre-operative only
procs <- c("Knee Replacement", "Groin Hernia")
comparison_data <- example_data[
  example_data$procedure %in% procs &
  example_data$time == "Pre-op", ]

# Add profile code and EQ-5D value
comparison_data$profile_code <- toEQ5Dindex(
  x         = comparison_data,
  dim.names = dim_names
)
comparison_data$value <- eq5d3l(
  comparison_data[, dim_names],
  country   = "UK",
  dim.names = dim_names
)

Profile comparison by group

eq5d_profile_level_summary_by_group(
  df           = comparison_data,
  names_eq5d   = dim_names,
  name_cat     = "procedure",
  eq5d_version = "3L"
)

EQ-5D value comparison

eq5d_utility_summary_by_group(
  df            = comparison_data,
  names_eq5d    = dim_names,
  name_groupvar = "procedure",
  eq5d_version  = "3L",
  country       = "UK"
)

eq5d_utility_by_group_plot(
  df            = comparison_data,
  names_eq5d    = dim_names,
  name_groupvar = "procedure",
  eq5d_version  = "3L",
  country       = "UK"
)$p +
  ggplot2::theme_minimal()

LSS analysis by group

The Level Sum Score (LSS) summarises overall severity as the sum of level scores across the five dimensions (range 5–15 for 3L, 5–25 for 5L). The plot below shows how EQ-5D values relate to LSS within each procedure group:

hernia_data <- comparison_data[
  comparison_data$procedure == "Groin Hernia", ]

eq5d_profile_lss_utility_plot(
  hernia_data,
  names_eq5d   = dim_names,
  eq5d_version = "3L",
  country      = "UK"
)$p +
  ggplot2::labs(
    title = "Groin Hernia",
    x     = "Level Sum Score (LSS)",
    y     = "EQ-5D value"
  ) +
  ggplot2::scale_x_continuous(limits = c(5, 15),
                               breaks = seq(5, 15, 2)) +
  ggplot2::theme_minimal()

knee_data <- comparison_data[
  comparison_data$procedure == "Knee Replacement", ]

eq5d_profile_lss_utility_plot(
  knee_data,
  names_eq5d   = dim_names,
  eq5d_version = "3L",
  country      = "UK"
)$p +
  ggplot2::labs(
    title = "Knee Replacement",
    x     = "Level Sum Score (LSS)",
    y     = "EQ-5D value"
  ) +
  ggplot2::scale_x_continuous(limits = c(5, 15),
                               breaks = seq(5, 15, 2)) +
  ggplot2::theme_minimal()

Further reading

• vignette("crosswalk-methods") — when and how to use crosswalk methods
• vignette("custom-value-sets") — adding and managing custom value sets
• vignette("update-value-sets") — keeping value sets up to date

References

Devlin N, Parkin D, Janssen B (2020). Methods for Analysing and Reporting EQ-5D Data. Springer, Cham. https://doi.org/10.1007/978-3-030-47622-9

eq5dsuite documentation built on May 14, 2026, 5:07 p.m.