# Handling and Analysing EQ-5d Data

## Documentation for package ‘eq5dsuite’ version 1.0.3

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

.add_utility | Add utility values to a data frame
.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.
.EQxwrprob | .EQxwrprob
.freqtab | Helper function for frequency of levels by dimensions tables
.gen_colours | Generate colours for PCHC figures
.getmode | Get the mode of a vector.
.get_lfs | Calculate the Level Frequency Score (LFS)
.get_names | Replace NULL names with default values
.modify_ggplot_theme | Modify ggplot2 theme
.pchc | Wrapper to determine Paretian Classification of Health Change
.pchctab | .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
.prep_eq5d | Data checking/preparation: EQ-5D variables
.prep_fu | Data checking/preparation: follow-up variable
.prep_vas | Data checking/preparation: VAS variable
.pstate3t5 | .pstate3t5
.pstate5t3 | .pstate5t3
.summary_cts_by_fu | Wrapper to summarise a continuous variable by follow-up (FU)
.summary_mean_ci | Wrapper to calculate summary mean with 95% confidence interval
.summary_table_2_1 | Wrapper for the repetitive code in function_table_2_1. Data frame summary
.summary_table_4_3 | Summary wrapper for Table 4.3
.summary_table_4_4 | Summary wrapper for Table 4.4
eq5d | eq5d
eq5d3l | eq5d3l
eq5d5l | eq5d5l
eq5dy3l | eq5dy3l
eqvs_add | eqvs_add
eqvs_display | eqvs_display
eqvs_drop | eqvs_drop
eqvs_load | eqvs_load
eqxw | eqxw
eqxwr | eqxwr
eqxw_UK | eqxw_UK
example_data | example_data
figure_1_2_1 | 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 | 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.
figure_1_2_3 | This function identifies respondents with a "Worsen" PCHC state (i.e., overall health state got worse between levels_fu[1] and levels_fu[2]), checks dimension-specific changes (e.g., mo_diff < 0), and summarizes by a grouping variable (e.g., procedure) and time points. It returns a data table and a ggplot object.
figure_1_2_4 | 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_5 | Figure 1.2.5: Health Profile Grid (HPG) for Two Time Points
figure_1_3_1 | Figure 1.3.1: EQ-5D values plotted against LSS
figure_1_3_2 | Figure 1.3.2: EQ-5D values plotted against LFS
figure_1_4_1 | Figure 1.4.1: Generate a Health State Density Curve (HSDC) for EQ-5D Data
figure_2_1 | Figure 2.1: EQ VAS scores
figure_2_2 | Figure 2.2: Mid-point EQ VAS scores
figure_3_1 | Figure 3.1: EQ-5D values by timepoints: mean values and 95% confidence intervals
figure_3_2 | Figure 3.2: Mean EQ-5D values and 95% confidence intervals: all vs by groupvar
figure_3_3 | Figure 3.3: EQ-5D values: smoothed lines and confidence intervals by groupvar
figure_3_4 | Figure 3.4: EQ-5D values: smoothed lines and confidence intervals by groupvar
figure_3_5 | 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
make_dummies | EQ_dummies
run_app | Launch the eq5dsuite Shiny Application
table_1_1_1 | Table 1.1.1: Frequency of levels by dimensions, cross-sectional
table_1_1_2 | Table 1.1.2: Frequency of levels by dimensions, separated by category
table_1_1_3 | Table 1.1.3: Prevalence of the 10 most frequently observed self-reported health states
table_1_2_1 | Table 1.2.1: Frequency of levels by dimensions, by follow-up
table_1_2_2 | Table 1.2.2: Changes in health according to the PCHC (Paretian Classification of Health Change)
table_1_2_3 | Table 1.2.3: Changes in health according to the PCHC, taking account of those with no problems
table_1_2_4 | Table 1.2.4: Changes in levels in each dimension, percentages of total and of type of change
table_1_3_1 | Table 1.3.1: Summary statistics for the EQ-5D values by all the different LSSs (Level Sum Scores)
table_1_3_2 | Table 1.3.2: Distribution of the EQ-5D states by LFS (Level Frequency Score)
table_1_3_3 | Table 1.3.3: Number of observations in the LFS (Level Frequency Score) according to the EQ-5D values
table_1_3_4 | Table 1.3.4: Summary statistics of EQ-5D values by LFS (Level Frequency Score)
table_2_1 | Table 2.1: EQ VAS Score by timepoints
table_2_2 | Table 2.2: EQ VAS Scores frequency of mid-points
table_3_1 | Table 3.1: EQ-5D values: by timepoints
table_3_2 | Table 3.2 EQ-5D values: by groupvar
table_3_3 | Table 3.3 EQ-5D values: by age and groupvar
toEQ5Ddims | toEQ5Ddims
toEQ5Dindex | Convert EQ-5D dimension scores to a five-digit profile index