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eq5dsuite

The goal of this package is to provide a suite of functions for manipulating and analysing EQ-5D data.

The file structure in the R folder is as follows:

init.R: description of the processes taking place every time the package is loaded, e.g. pre-calculation of the crosswalk matrices; based on the analogous file from the eqxwr package.
utils.R; EQ_functions.R; eqxwr.R - files based on the files with the same name as those from the eqxwr package but expanded to accept 3L data
eqxw.R - file containing forward crosswalk functions; adapted from eqxwr.R
eq5d_devlin.R - file containing code for Tables and Figures from the Devlin book (one file per Table or Figure)
eq5d_aux.R - secondary files, e.g. wrappers, used in eq5d_devlin.R

In addition,

data/example_data.rda is an example dataset that can be used to test eq5d_devlin.R functions

Installation

You can install the development version of eq5dsuite from GitHub with:

# install.packages("devtools")
devtools::install_github("MathsInHealth/eq5dsuite")

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Version

Install

install.packages('eq5dsuite')

Monthly Downloads

238

Version

1.0.1

License

GPL (>= 2)

Maintainer

Kim Rand

Last Published

June 24th, 2025

Functions in eq5dsuite (1.0.1)

Figure 1.2.5: Health Profile Grid (HPG) for Two Time Points

Figure 1.3.1: EQ-5D values plotted against LSS

.summary_cts_by_fu

Wrapper to summarise a continuous variable by follow-up (FU)

.summary_table_4_3

Summary wrapper for Table 4.3

Figure 1.2.1: Paretian Classification of Health Change (PCHC) by Group This function computes PCHC categories between two time points for each subject, stratifies them by a grouping variable, and produces a single bar chart with side-by-side bars showing the distribution of PCHC categories.

Figure 1.2.2: Percentage of Respondents Who Improved in Each EQ-5D Dimension, by Group This function calculates how many respondents improved in each dimension between two time points and summarizes the results for each group. The, it prodcuces a dimension-focused chart illustrating improvement percentages by dimension.

.summary_table_4_4

Summary wrapper for Table 4.4

Figure 3.1: EQ-5D values by timepoints: mean values and 95% confidence intervals

Figure 3.2: Mean EQ-5D values and 95% confidence intervals: all vs by groupvar

Figure 2.2: Mid-point EQ VAS scores

Table 1.2.4: Changes in levels in each dimension, percentages of total and of type of change

Figure 2.1: EQ VAS scores

Table 1.3.1: Summary statistics for the EQ-5D values by all the different LSSs (Level Sum Scores)

Figure 1.2.4: Percentage of Respondents Who Had a Mixed Change Overall, by Dimension Improved or Worsened, Grouped by Procedure (or Other Grouping)

Figure 1.2.3: Percentage of Respondents Who Worsened in Each EQ-5D Dimension, by Group

Table 1.1.1: Frequency of levels by dimensions, cross-sectional

Table 1.1.2: Frequency of levels by dimensions, separated by category

make_all_EQ_states

make_all_EQ_states

Figure 3.5: EQ-5D values: smoothed lines and confidence intervals by groupvar

make_all_EQ_indexes

make_all_EQ_indexes

Figure 3.3: EQ-5D values: smoothed lines and confidence intervals by groupvar

Table 1.3.4: Summary statistics of EQ-5D values by LFS (Level Frequency Score)

Table 2.1: EQ VAS Score by timepoints

Table 3.2 EQ-5D values: by groupvar

Table 3.3 EQ-5D values: by age and groupvar

Table 2.2: EQ VAS Scores frequency of mid-points

Table 3.1: EQ-5D values: by timepoints

Table 1.1.3: Prevalence of the 10 most frequently observed self-reported health states

Table 1.2.1: Frequency of levels by dimensions, by follow-up

Table 1.3.2: Distribution of the EQ-5D states by LFS (Level Frequency Score)

Figure 3.4: EQ-5D values: smoothed lines and confidence intervals by groupvar

Table 1.3.3: Number of observations in the LFS (Level Frequency Score) according to the EQ-5D values

Figure 1.3.2: EQ-5D values plotted against LFS

Figure 1.4.1: Generate a Health State Density Curve (HSDC) for EQ-5D Data

Table 1.2.2: Changes in health according to the PCHC (Paretian Classification of Health Change)

Table 1.2.3: Changes in health according to the PCHC, taking account of those with no problems

Add utility values to a data frame

Generate colours for PCHC figures

Replace NULL names with default values

Get the mode of a vector.

.check_uniqueness

Check the uniqueness of groups This function takes a data frame `df` and a vector of columns `group_by`, and checks whether the combinations of values in the columns specified by `group_by` are unique. If the combinations are not unique, a warning message is printed.

Helper function for frequency of levels by dimensions tables

.modify_ggplot_theme

Modify ggplot2 theme

Wrapper to determine Paretian Classification of Health Change

Calculate the Level Frequency Score (LFS)

.pchctab: Changes in health according to the PCHC (Paretian Classification of Health Change)

.pchc_plot_by_dim

Wrapper to generate Paretian Classification of Health Change plot by dimension

.summary_mean_ci

Wrapper to calculate summary mean with 95% confidence interval

Data checking/preparation: VAS variable

Data checking/preparation: EQ-5D variables

Data checking/preparation: follow-up variable

.summary_table_2_1

Wrapper for the repetitive code in function_table_2_1. Data frame summary