forked from fragla/eq5d
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME.Rmd
283 lines (183 loc) · 13.3 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
<!-- badges: start -->
[](https://github.com/fragla/eq5d/actions)
[](https://ci.appveyor.com/project/fragla/eq5d)
[](https://app.codecov.io/gh/fragla/eq5d?branch=master)
<!-- badges: end -->
# EQ-5D
EQ-5D is a popular health related quality of life instrument used in the clinical and economic evaluation of health care. Developed by the [EuroQol](https://euroqol.org/) group, the instrument consists of two components: health state description and evaluation.
For the description component a subject self-rates their health in terms of five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression using either a three-level ([EQ-5D-3L](https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/) and [EQ-5D-Y](https://euroqol.org/eq-5d-instruments/eq-5d-y-about/)) or a five-level ([EQ-5D-5L](https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/)) scale.
The evaluation component requires a patient to record their overall health status using a visual analogue scale (EQ-VAS).
Following assessment the scores from the descriptive component can be reported as a five digit number ranging from 11111 (full health) to 33333/55555 (worst health). A number of methods exist for analysing these five digit profiles. However, frequently they are converted to a single utility index using country specific value sets, which can be used in the clinical and economic evaluation of health care as well as in population health surveys.
The eq5d package provides methods for the cross-sectional and longitudinal analysis of EQ-5D profiles and also the calculation of utility index scores from a subject's dimension scores. Additionally, a [Shiny](https://shiny.rstudio.com) app is included to enable the calculation, visualisation and automated statistical analysis of multiple EQ-5D index values via a web browser using EQ-5D dimension scores stored in CSV or Excel files.
Value sets for EQ-5D-3L are available for many countries and have been produced using the time trade-off (TTO) valuation technique or the visual analogue scale (VAS) valuation technique. Some countries have TTO and VAS value sets for EQ-5D-3L. Additionally, EQ-5D-3L "reverse crosswalk" value sets published on the [EuroQol](https://euroqol.org/support/tools/analysis-tools/cross-walk-reverse-cross-walk/) website that enable EQ-5D-3L data to be mapped to EQ-5D-5L value sets are included.
For EQ-5D-5L, a standardised valuation study protocol (EQ-VT) was developed by the EuroQol group based on the composite time trade-off (cTTO) valuation technique supplemented by a discrete choice experiment (DCE). The EuroQol group recommends users to use a standard value set where available.
The EQ-5D-5L "crosswalk" value sets published by [van Hout *et al*](https://pubmed.ncbi.nlm.nih.gov/22867780/) as well as that for Russia are also included. The crosswalk value sets enable index values to be calculated for EQ-5D-5L data where no value set is available by mapping between the EQ-5D-5L and EQ-5D-3L descriptive systems.
Additional information on EQ-5D can be found on the [EuroQol](https://euroqol.org/) website as well as in [Szende *et al* (2007)](https://doi.org/10.1007/1-4020-5511-0) and [Szende
*et al* (2014)](https://doi.org/10.1007/978-94-007-7596-1). Advice on [choosing a value set](https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/valuation/choosing-a-value-set/
) can also be found on the EuroQol website.
## Installation
You can install the released version of eq5d from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("eq5d")
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("fragla/eq5d")
```
## Quick Start
```{r quickStart}
library(eq5d)
#single calculation
#named vector MO, SC, UA, PD and AD represent mobility, self-care, usual activites, pain/discomfort and anxiety/depression, respectfully.
scores <- c(MO=1,SC=2,UA=3,PD=2,AD=1)
#EQ-5D-3L using the UK TTO value set
eq5d(scores=scores, country="UK", version="3L", type="TTO")
#Using five digit format
eq5d(scores=12321, country="UK", version="3L", type="TTO")
#EQ-5D-Y using the Slovenian cTTO value set
eq5d(scores=13321, country="Slovenia", version="Y", type="cTTO")
#EQ-5D-5L crosswalk
eq5d(scores=55555, country="Spain", version="5L", type="CW")
#EQ-5D-3L reverse crosswalk
eq5d(scores=33333, country="Germany", version="3L", type="RCW")
#multiple calculations using the Canadian VT value set
#data.frame with individual dimensions
scores.df <- data.frame(
MO=c(1,2,3,4,5), SC=c(1,5,4,3,2), UA=c(1,5,2,3,1), PD=c(1,3,4,3,4), AD=c(1,2,1,2,1)
)
eq5d(scores.df, country="Canada", version="5L", type="VT")
#data.frame using five digit format
scores.df2 <- data.frame(state=c(11111,25532,34241,43332,52141))
eq5d(scores.df2, country="Canada", version="5L", type="VT", five.digit="state")
#or using a vector
eq5d(scores.df2$state, country="Canada", version="5L", type="VT")
```
## Value sets
The available value sets can be viewed using the ***valuesets*** function. The results can be filtered by EQ-5D version, value set type or by country.
```{r valuesets}
# Return all value sets (top 6 returned for brevity).
head(valuesets())
# Return VAS based value sets (top 6 returned for brevity).
head(valuesets(type="VAS"))
# Return EQ-5D-5L value sets (top 6 returned for brevity).
head(valuesets(version="5L"))
# Return all UK value sets.
valuesets(country="UK")
```
## Analysis of EQ-5D Profiles
A number of methods have been published that enable the analysis of EQ-5D profiles, most recently in the open access book Methods for Analysing and Reporting EQ-5D Data by [Devlin, Janssen and Parkin](https://www.springer.com/gp/book/9783030476212). The eq5d package includes R implentations of some of the methods from this book and from other sources that may be of use in analysing EQ-5D data.
### Cumulative frequency analysis
The ***eq5dcf*** function calculates the frequency, percentage, cumulative frequency and cumulative percentage for each five digit profile in an EQ-5D dataset. Either a vector of five digit profiles or a data.frame of indiviual dimensions can be passed to this function in order to summarise data in this way.
```{r eq5dcf}
library(readxl)
#load example data
data <- read_excel(system.file("extdata", "eq5d3l_example.xlsx", package="eq5d"))
#run eq5dcf function on a data.frame
res <- eq5dcf(data, "3L")
# Return data.frame of cumulative frequency stats (top 6 returned for brevity).
head(res)
```
### Summarising the Severity of EQ-5D Profiles
The eq5d package includes methods for summarising the severity of EQ-5D health state. The Level Sum Score (LSS) treats each dimension's level as a number rather than a category. Each number is added up to produce a score between 5 and 15 for EQ-5D-3L and 5 and 25 for EQ-5D-5L.
```{r eq5dlss}
lss(c(MO=1,SC=2,UA=3,PD=2,AD=1), version="3L")
lss(55555, version="5L")
lss(c(11111,12345, 55555), version="5L")
```
The Level Frequency Score (LFS) is an alternative method of summarising profile data developed by [Oppe and de Charro](https://pubmed.ncbi.nlm.nih.gov/11189116). Here the frequency of the levels for each health state are characterised. As described in Devlin, Janssen and Parkin's book, the full health profile 11111 for EQ-5D-5L has 5, 1 s, no level 2, 3, 4 and 5s, so the LFS is 50000; the health profile 55555 is 00005; profiles such as 31524 and 53412 would be 11111.
```{r eq5dlfs}
lfs(c(MO=1,SC=2,UA=3,PD=2,AD=1), version="3L")
lfs(55555, version="5L")
lfs(c(11111,12345, 55555), version="5L")
```
### Paretian Classification of Health Change
The Paretian Classification of Health Change (PCHC) was developed by [Devlin et al](https://pubmed.ncbi.nlm.nih.gov/20623685) in 2010 and is used to compare changes in individuals over time. PCHC classifies the change in an individual's health state as better (improvement in at least one dimension), worse (a deterioration in at least one dimension), mixed (improvements and deteriorations in dimensions) or there being no change in the health state. Those classified in the No change group with the 11111 health state can be separated into their own "No problems" group. PCHC can be calculated using the ***pchc*** function.
```{r pchc}
library(readxl)
#load example data
data <- read_excel(system.file("extdata", "eq5d3l_example.xlsx", package="eq5d"))
#use first 50 entries of each group as pre/post
pre <- data[data$Group=="Group1",][1:50,]
post <- data[data$Group=="Group2",][1:50,]
#run pchc function on data.frames
#Show no change, improve, worse, mixed without totals
res1 <- pchc(pre, post, version="3L", no.problems=FALSE, totals=FALSE)
res1
#Show totals, but not those with no problems
res2 <- pchc(pre, post, version="3L", no.problems=FALSE, totals=TRUE)
res2
#Show totals and no problems for each dimension
res3 <- pchc(pre, post, version="3L", no.problems=TRUE, totals=TRUE, by.dimension=TRUE)
res3
```
### EQ-5D-DS
The ***eq5dds*** function is an R approximation of the Stata command written by [Ramos-Goñi & Ramallo-Fariña](https://www.stata-journal.com/article.html?article=st0450). The function analyses and summarises the descriptive components of an EQ-5D dataset. The "by" argument enables a grouping variable to be specified when analysing the data subgroup.
```{r eq5dds}
dat <- data.frame(
matrix(
sample(1:3,5*12, replace=TRUE),12,5,
dimnames=list(1:12,c("MO","SC","UA","PD","AD"))
),
Sex=rep(c("Male", "Female"))
)
eq5dds(dat, version="3L")
eq5dds(dat, version="3L", counts=TRUE)
eq5dds(dat, version="3L", by="Sex")
```
## Helper functions
Helper functions are included, which may be useful in the processing of EQ-5D data. ***getHealthStates*** returns a vector of all possible five digit health states for a specified EQ-5D version. ***getDimensionsFromHealthStates*** splits a vector of five digit health states into a data.frame of their individual components and ***getHealthStatesFromDimensions*** combines indiviual dimensions in a data.frame into five digit health states.
```{r helper}
# Get all EQ-5D-3L five digit health states (top 6 returned for brevity).
head(getHealthStates("3L"))
# Split five digit health states into their individual components.
getDimensionsFromHealthStates(c("12345", "54321"), version="5L")
```
## Example data
Example data is included with the package and can be accessed using the ***system.file*** function.
```{r example data}
# View example files.
dir(path=system.file("extdata", package="eq5d"))
# Read example EQ-5D-3L data.
library(readxl)
data <- read_excel(system.file("extdata", "eq5d3l_example.xlsx", package="eq5d"))
# Calculate index scores
scores <- eq5d(data, country="UK", version="3L", type="TTO")
# Top 6 scores
head(scores)
```
## Shiny web interface
The calculation (and visualisation) of multiple EQ-5D indices can also be performed by upload of a CSV or Excel file using the packaged [Shiny](https://shiny.rstudio.com) app. This requires the [shiny](https://cran.r-project.org/package=shiny), [DT](https://cran.r-project.org/package=DT),
[FSA](https://cran.r-project.org/package=FSA),
[ggplot2](https://cran.r-project.org/package=ggplot2),
[ggiraph](https://cran.r-project.org/package=ggiraph),
[ggiraphExtra](https://cran.r-project.org/package=ggiraphExtra),
[mime](https://cran.r-project.org/package=mime), [PMCMRplus](https://cran.r-project.org/package=PMCMRplus), [readxl](https://cran.r-project.org/package=readxl),
[shinycssloaders](https://cran.r-project.org/package=shinycssloaders) and
[shinyWidgets](https://cran.r-project.org/package=shinyWidgets) packages. Ideally the CSV/Excel headers should be the same as the names of the vector passed to the ***eq5d*** function i.e. MO, SC, UA, PD and AD or the column name "State" if using the five digit format. However, a modal dialog will prompt the user to select the appropriate columns if the defaults can not be found. Both files below will produce the same results.
The app is launched using the ***shiny_eq5d*** function.
```{r shiny, echo=TRUE, eval=FALSE}
shiny_eq5d()
```
Alternatively, it can be accessed without installing R/Shiny/eq5d by visiting [shinyapps.io](https://fragla.shinyapps.io/shiny-eq5d).
## License
This project is licensed under the MIT License - see the [LICENSE.md](https://github.com/fragla/eq5d/blob/master/LICENSE.md) file for details.