Manipulating and Analysing EQ-5d Data
Table 3.2 EQ-5D values: by groupvar
Figure 1.2.4: Percentage of respondents who had a mixed change by the ...
Figure 1.3.1: EQ-5D values plotted against LSS
Figure 1.3.2: EQ-5D values plotted against LFS
Figure 2.1: EQ VAS scores
Figure 2.2: Mid-point EQ VAS scores
Figure 3.1: EQ-5D values by timepoints: mean values and 95% confidence...
Figure 3.2: Mean EQ-5D values and 95% confidence intervals: all vs by ...
Table 1.2.1: Frequency of levels by dimensions, by follow-up
Table 1.2.2: Changes in health according to the PCHC (Paretian Classif...
Table 1.2.3: Changes in health according to the PCHC, taking account o...
Table 1.2.4: Changes in levels in each dimension, percentages of total...
Table 1.3.1: Summary statistics for the EQ-5D values by all the differ...
Table 1.3.2: Distribution of the EQ-5D states by LFS (Level Frequency ...
Table 1.3.3: Number of observations in the LFS (Level Frequency Score)...
Table 1.3.4: Summary statistics of EQ-5D values by LFS (Level Frequenc...
Table 2.1: EQ VAS Score by timepoints
Table 2.2: EQ VAS Scores frequency of mid-points
Table 3.1: EQ-5D values: by timepoints
Replace NULL names with default values
Get the mode of a vector.
Modify ggplot2 theme
eq5d3l
eq5d5l
eq5dy3l
Figure 1.2.3: Percentage of respondents who worsened overall by the di...
Generate colours for PCHC figures
Calculate the Level Frequency Score (LFS)
.pstate3t5
.pstate5t3
Add utility values to a data frame
Check the uniqueness of groups This function takes a data frame df
a...
.EQxwrprob
Helper function for frequency of levels by dimensions tables
Table 1.1.3: Prevalence of the 10 most frequently observed self-report...
Wrapper to determine Paretian Classification of Health Change
Wrapper to generate Paretian Classification of Health Change plot by d...
.pchctab: Changes in health according to the PCHC (Paretian Classifica...
Data checking/preparation: EQ-5D variables
Data checking/preparation: follow-up variable
Data checking/preparation: VAS variable
Wrapper to summarise a continuous variable by follow-up (FU)
Wrapper to calculate summary mean with 95% confidence interval
Wrapper for the repetitive code in function_table_2_1. Data frame summ...
Summary wrapper for Table 4.3
Summary wrapper for Table 4.4
eq5d
eqvs_add
eqvs_display
eqvs_drop
eqvs_load
eqxw
eqxwr
Figure 1.2.1: Paretian Classification of Health Change
Figure 1.2.2: Percentage of respondents who improved overall by the di...
Figure 3.3: EQ-5D values: smoothed lines and confidence intervals by g...
Figure 3.4: EQ-5D values: smoothed lines and confidence intervals by g...
Figure 3.5: EQ-5D values: smoothed lines and confidence intervals by g...
make_all_EQ_indexes
make_all_EQ_states
EQ_dummies
Table 1.1.1: Frequency of levels by dimensions, cross-sectional
Table 1.1.2: Frequency of levels by dimensions, separated by category
Table 3.3 EQ-5D values: by age and groupvar
toEQ5Ddims
toEQ5DIndex
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>.