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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

1.0.0

License

GPL (>= 2)

Maintainer

Kim Rand

Last Published

May 21st, 2024

Functions in eq5dsuite (1.0.0)

Data checking/preparation: follow-up variable

Data checking/preparation: EQ-5D variables

Figure 1.3.2: EQ-5D values plotted against LFS

.summary_table_4_3

Summary wrapper for Table 4.3

Figure 1.2.4: Percentage of respondents who had a mixed change by the dimensions in which they improved and worsened (%)

Figure 2.1: EQ VAS scores

Figure 1.3.1: EQ-5D values plotted against LSS

.summary_table_4_4

Summary wrapper for Table 4.4

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

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

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

make_all_EQ_indexes

make_all_EQ_indexes

make_all_EQ_states

make_all_EQ_states

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

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

Table 2.1: EQ VAS Score by timepoints

Figure 1.2.1: Paretian Classification of Health Change

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

.summary_cts_by_fu

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

Figure 1.2.2: Percentage of respondents who improved overall by the dimensions (%)

Table 2.2: EQ VAS Scores frequency of mid-points

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

Table 3.1: EQ-5D values: by timepoints

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

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

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

Table 3.2 EQ-5D values: by groupvar

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

Figure 1.2.3: Percentage of respondents who worsened overall by the dimensions (%)

Figure 2.2: Mid-point EQ VAS scores

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

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

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

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

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

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

.modify_ggplot_theme

Modify ggplot2 theme

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.

Generate colours for PCHC figures

Replace NULL names with default values

Wrapper to determine Paretian Classification of Health Change

Helper function for frequency of levels by dimensions tables

Calculate the Level Frequency Score (LFS)

Data checking/preparation: VAS variable

.summary_table_2_1

Wrapper for the repetitive code in function_table_2_1. Data frame summary

.summary_mean_ci

Wrapper to calculate summary mean with 95% confidence interval

Add utility values to a data frame

.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