Package: eq5dsuite 1.0.0

eq5dsuite: Manipulating and Analysing EQ-5d Data

The EQ-5D is a widely-used standarized instrument for measuring Health Related Quality Of Life (HRQOL), developed by the EuroQol group <https://euroqol.org/>. It assesses five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, using either a three-level (EQ-5D-3L) or five-level (EQ-5D-5L) scale. Scores from these dimensions are commonly converted into a single utility index using country-specific value sets, which are critical in clinical and economic evaluations of healthcare and in population health surveys. The eq5dsuite package enables users to calculate utility index values for the EQ-5D instruments, including crosswalk utilities using the original crosswalk developed by van Hout et al. (2012) <doi:10.1016/j.jval.2012.02.008> (mapping EQ-5D-5L responses to EQ-5D-3L index values), or the recently developed reverse crosswalk by van Hout et al. (2021) <doi:10.1016/j.jval.2021.03.009> (mapping EQ-5D-3L responses to EQ-5D-5L index values). Users are allowed to add and/or remove user-defined value sets. Additionally, the package provides tools to analyze EQ-5D data according to the recommended guidelines outlined in "Methods for Analyzing and Reporting EQ-5D data" by Devlin et al. (2020) <doi:10.1007/978-3-030-47622-9>.

Authors:Kim Rand [aut, cre], Iryna Schlackow [aut], Anabel Estévez-Carrillo [aut]

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eq5dsuite.pdf |eq5dsuite.html✨
eq5dsuite/json (API)

# Install 'eq5dsuite' in R:
install.packages('eq5dsuite', repos = c('https://mathsinhealth.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mathsinhealth/eq5dsuite/issues

Datasets:

On CRAN:

62 exports 1.59 score 38 dependencies 1 dependents 182 downloads

Last updated 4 months agofrom:80687ae218. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 20 2024
R-4.5-winNOTEAug 20 2024
R-4.5-linuxNOTEAug 20 2024
R-4.4-winNOTEAug 20 2024
R-4.4-macNOTEAug 20 2024
R-4.3-winNOTEAug 20 2024
R-4.3-macNOTEAug 20 2024

Exports:.add_utility.check_uniqueness.gen_colours.get_lfs.get_names.getmode.modify_ggplot_theme.pchc.pchc_plot_by_dim.pchctab.prep_eq5d.prep_fu.prep_vas.summary_cts_by_fu.summary_mean_ci.summary_table_2_1.summary_table_4_3.summary_table_4_4eq5deq5d3leq5d5leq5dy3leqvs_addeqvs_displayeqvs_dropeqvs_loadeqxweqxwrfigure_1_2_1figure_1_2_2figure_1_2_3figure_1_2_4figure_1_3_1figure_1_3_2figure_2_1figure_2_2figure_3_1figure_3_2figure_3_3figure_3_4figure_3_5make_all_EQ_indexesmake_all_EQ_statesmake_dummiestable_1_1_1table_1_1_2table_1_1_3table_1_2_1table_1_2_2table_1_2_3table_1_2_4table_1_3_1table_1_3_2table_1_3_3table_1_3_4table_2_1table_2_2table_3_1table_3_2table_3_3toEQ5DdimstoEQ5Dindex

Dependencies:clicolorspacecpp11dplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmomentsmunsellnlmepillarpkgconfigpurrrR6rappdirsRColorBrewerrlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Add utility values to a data frame.add_utility
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..check_uniqueness
.EQxwrprob.EQxwrprob
Helper function for frequency of levels by dimensions tables.freqtab
Generate colours for PCHC figures.gen_colours
Calculate the Level Frequency Score (LFS).get_lfs
Replace NULL names with default values.get_names
Get the mode of a vector..getmode
Modify ggplot2 theme.modify_ggplot_theme
Wrapper to determine Paretian Classification of Health Change.pchc
Wrapper to generate Paretian Classification of Health Change plot by dimension.pchc_plot_by_dim
.pchctab: Changes in health according to the PCHC (Paretian Classification of Health Change).pchctab
Data checking/preparation: EQ-5D variables.prep_eq5d
Data checking/preparation: follow-up variable.prep_fu
Data checking/preparation: VAS variable.prep_vas
.pstate3t5.pstate3t5
.pstate5t3.pstate5t3
Wrapper to summarise a continuous variable by follow-up (FU).summary_cts_by_fu
Wrapper to calculate summary mean with 95% confidence interval.summary_mean_ci
Wrapper for the repetitive code in function_table_2_1. Data frame summary.summary_table_2_1
Summary wrapper for Table 4.3.summary_table_4_3
Summary wrapper for Table 4.4.summary_table_4_4
eq5deq5d
eq5d3leq5d3l
eq5d5leq5d5l
eq5dy3leq5dy3l
eqvs_addeqvs_add
eqvs_displayeqvs_display
eqvs_dropeqvs_drop
eqvs_loadeqvs_load
eqxweqxw
eqxwreqxwr
example_dataexample_data
Figure 1.2.1: Paretian Classification of Health Changefigure_1_2_1
Figure 1.2.2: Percentage of respondents who improved overall by the dimensions (%)figure_1_2_2
Figure 1.2.3: Percentage of respondents who worsened overall by the dimensions (%)figure_1_2_3
Figure 1.2.4: Percentage of respondents who had a mixed change by the dimensions in which they improved and worsened (%)figure_1_2_4
Figure 1.3.1: EQ-5D values plotted against LSSfigure_1_3_1
Figure 1.3.2: EQ-5D values plotted against LFSfigure_1_3_2
Figure 2.1: EQ VAS scoresfigure_2_1
Figure 2.2: Mid-point EQ VAS scoresfigure_2_2
Figure 3.1: EQ-5D values by timepoints: mean values and 95% confidence intervalsfigure_3_1
Figure 3.2: Mean EQ-5D values and 95% confidence intervals: all vs by groupvarfigure_3_2
Figure 3.3: EQ-5D values: smoothed lines and confidence intervals by groupvarfigure_3_3
Figure 3.4: EQ-5D values: smoothed lines and confidence intervals by groupvarfigure_3_4
Figure 3.5: EQ-5D values: smoothed lines and confidence intervals by groupvarfigure_3_5
make_all_EQ_indexesmake_all_EQ_indexes
make_all_EQ_statesmake_all_EQ_states
EQ_dummiesmake_dummies
Table 1.1.1: Frequency of levels by dimensions, cross-sectionaltable_1_1_1
Table 1.1.2: Frequency of levels by dimensions, separated by categorytable_1_1_2
Table 1.1.3: Prevalence of the 10 most frequently observed self-reported health statestable_1_1_3
Table 1.2.1: Frequency of levels by dimensions, by follow-uptable_1_2_1
Table 1.2.2: Changes in health according to the PCHC (Paretian Classification of Health Change)table_1_2_2
Table 1.2.3: Changes in health according to the PCHC, taking account of those with no problemstable_1_2_3
Table 1.2.4: Changes in levels in each dimension, percentages of total and of type of changetable_1_2_4
Table 1.3.1: Summary statistics for the EQ-5D values by all the different LSSs (Level Sum Scores)table_1_3_1
Table 1.3.2: Distribution of the EQ-5D states by LFS (Level Frequency Score)table_1_3_2
Table 1.3.3: Number of observations in the LFS (Level Frequency Score) according to the EQ-5D valuestable_1_3_3
Table 1.3.4: Summary statistics of EQ-5D values by LFS (Level Frequency Score)table_1_3_4
Table 2.1: EQ VAS Score by timepointstable_2_1
Table 2.2: EQ VAS Scores frequency of mid-pointstable_2_2
Table 3.1: EQ-5D values: by timepointstable_3_1
Table 3.2 EQ-5D values: by groupvartable_3_2
Table 3.3 EQ-5D values: by age and groupvartable_3_3
toEQ5DdimstoEQ5Ddims
toEQ5DIndextoEQ5Dindex