- Notifications
You must be signed in to change notification settings - Fork6
License
Unknown, MIT licenses found
Licenses found
fragla/eq5d
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
EQ-5D is a popular health related quality of life instrument used in theclinical and economic evaluation of health care. Developed by theEuroQol group, the instrument consists of twocomponents: health state description and evaluation.
For the description component a subject self-rates their health in termsof five dimensions; mobility, self-care, usual activities,pain/discomfort, and anxiety/depression using either a three-level(EQ-5D-3LandEQ-5D-Y-3L)or a five-level(EQ-5D-5L)scale.
The evaluation component requires a patient to record their overallhealth status using a visual analogue scale (EQ-VAS).
Following assessment the scores from the descriptive component can bereported as a five digit number ranging from 11111 (full health) to33333/55555 (worst health). A number of methods exist for analysingthese five digit profiles. However, frequently they are converted to asingle utility index using country specific value sets, which can beused in the clinical and economic evaluation of health care as well asin population health surveys.
The eq5d package provides methods for the cross-sectional andlongitudinal analysis of EQ-5D profiles and also the calculation ofutility index scores from a subject’s dimension scores. Additionally, aShiny app is included to enable thecalculation, visualisation and automated statistical analysis ofmultiple EQ-5D index values via a web browser using EQ-5D dimensionscores stored in CSV or Excel files.
Value sets for EQ-5D-3L are available for many countries and have beenproduced using the time trade-off (TTO) valuation technique or thevisual analogue scale (VAS) valuation technique. Some countries have TTOand VAS value sets for EQ-5D-3L. Additionally, EQ-5D-3L “reversecrosswalk” value sets based on thevan Houtet al(2021) models as well asthose published on theEuroQolwebsite that enable EQ-5D-3L data to be mapped to EQ-5D-5L value setsare included.
For EQ-5D-5L, a standardised valuation study protocol (EQ-VT) wasdeveloped 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 setwhere available.
The EQ-5D-5L “crosswalk” value sets published byvan Houtet al(2012) as well as that forRussia are also included. The crosswalk value sets enable index valuesto be calculated for EQ-5D-5L data where no value set is available bymapping between the EQ-5D-5L and EQ-5D-3L descriptive systems.
The recently published age and sex conditional based mapping data by theNICE Decision SupportUnitare also now part of the package. These enable age-sex based EQ-5D-3L toEQ-5D-5L and EQ-5D-5L to EQ-5D-3L mappings from dimensions and exact orapproximate utility index scores.
Additional information on EQ-5D can be found on theEuroQol website as well as inSzendeet al(2007) andSzendeet al(2014). Advice onchoosinga valuesetcan also be found on the EuroQol website.
You can install the released version of eq5d fromCRAN with:
install.packages("eq5d")And the development version fromGitHub with:
# install.packages("devtools")devtools::install_github("fragla/eq5d")
library(eq5d)#> Loading required package: lifecycle#> Loading required package: rlang#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 seteq5d(scores=scores,country="UK",version="3L",type="TTO")#> [1] 0.329#Using five digit formateq5d(scores=12321,country="UK",version="3L",type="TTO")#> [1] 0.329#EQ-5D-Y-3L using the Slovenian value seteq5d(scores=13321,country="Slovenia",version="Y3L")#> [1] 0.295#EQ-5D-5L crosswalkeq5d(scores=55555,country="Spain",version="5L",type="CW")#> [1] -0.654#EQ-5D-3L reverse crosswalkeq5d(scores=33333,country="Germany",version="3L",type="RCW")#> [1] -0.495#EQ-5D-5L to EQ-5D-3L NICE DSU mapping#Using dimensionseq5d(c(MO=1,SC=2,UA=3,PD=4,AD=5),version="5L",type="DSU",country="UK",age=23,sex="male")#> [1] 0.083#Using exact utility scoreeq5d(0.922,country="UK",version="5L",type="DSU",age=18,sex="male")#> [1] 0.893#Using approximate utility scoreeq5d(0.435,country="UK",version="5L",type="DSU",age=30,sex="female",bwidth=0.0001)#> [1] 0.302#multiple calculations using the Canadian VT value set#data.frame with individual dimensionsscores.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")#> [1] 0.949 0.362 0.390 0.524 0.431#data.frame using five digit formatscores.df2<-data.frame(state=c(11111,25532,34241,43332,52141))eq5d(scores.df2,country="Canada",version="5L",type="VT",five.digit="state")#> [1] 0.949 0.362 0.390 0.524 0.431#or using a vectoreq5d(scores.df2$state,country="Canada",version="5L",type="VT")#> [1] 0.949 0.362 0.390 0.524 0.431
The available value sets can be viewed using thevaluesetsfunction. The results can be filtered by EQ-5D version, value set typeor by country.
# Return TTO value sets with PubMed IDs and DOIs (top 6 returned for brevity).head(valuesets(type="TTO",references=c("PubMed","DOI")))#> Version Type Country PubMed DOI#> 1 EQ-5D-3L TTO Argentina 19900257 10.1111/j.1524-4733.2008.00468.x#> 2 EQ-5D-3L TTO Australia 21914515 10.1016/j.jval.2011.04.009#> 3 EQ-5D-3L TTO Bermuda 38982011 10.1007/s10198-024-01701-2#> 4 EQ-5D-3L TTO Brazil 29702778 10.1016/j.vhri.2013.01.009#> 5 EQ-5D-3L TTO Canada 22328929 10.1371/journal.pone.0031115#> 6 EQ-5D-3L TTO Chile 22152184 10.1016/j.jval.2011.09.002.# Return VAS value sets with ISBN and external URL (top 6 returned for brevity).head(valuesets(type="VAS",references=c("ISBN","ExternalURL")))#> Version Type Country ISBN#> 1 EQ-5D-3L VAS Belgium 1-4020-5511-0#> 2 EQ-5D-3L VAS Denmark 1-4020-5511-0#> 3 EQ-5D-3L VAS Europe 1-4020-5511-0#> 4 EQ-5D-3L VAS Finland 1-4020-5511-0#> 5 EQ-5D-3L VAS Germany 1-4020-5511-0#> 6 EQ-5D-3L VAS Iran <NA>#> ExternalURL#> 1 https://eq-5dpublications.euroqol.org/download?id=0_54011&fileId=54420#> 2 https://eq-5dpublications.euroqol.org/download?id=0_54011&fileId=54420#> 3 https://eq-5dpublications.euroqol.org/download?id=0_54011&fileId=54420#> 4 https://eq-5dpublications.euroqol.org/download?id=0_54011&fileId=54420#> 5 https://eq-5dpublications.euroqol.org/download?id=0_54011&fileId=54420#> 6 <NA># Return EQ-5D-5L value sets (top 6 returned for brevity).head(valuesets(version="5L"))#> Version Type Country PubMed DOI ISBN ExternalURL#> 1 EQ-5D-5L CW Bermuda 38982011 10.1007/s10198-024-01701-2 <NA> <NA>#> 2 EQ-5D-5L CW Denmark 22867780 10.1016/j.jval.2012.02.008 <NA> <NA>#> 3 EQ-5D-5L CW France 22867780 10.1016/j.jval.2012.02.008 <NA> <NA>#> 4 EQ-5D-5L CW Germany 22867780 10.1016/j.jval.2012.02.008 <NA> <NA>#> 5 EQ-5D-5L CW Japan 22867780 10.1016/j.jval.2012.02.008 <NA> <NA>#> 6 EQ-5D-5L CW Jordan 39225720 10.1007/s10198-024-01712-z <NA> <NA># Return all French value sets.valuesets(country="France")#> Version Type Country PubMed DOI ISBN ExternalURL#> 1 EQ-5D-3L TTO France 21935715 10.1007/s10198-011-0351-x <NA> <NA>#> 2 EQ-5D-5L CW France 22867780 10.1016/j.jval.2012.02.008 <NA> <NA>#> 3 EQ-5D-5L VT France 31912325 10.1007/s40273-019-00876-4 <NA> <NA>#> 4 EQ-5D-3L RCW France 34452708 10.1016/j.jval.2021.03.009 <NA> <NA>#> Notes#> 1 <NA>#> 2 <NA>#> 3 <NA>#> 4 van Hout (2021)# Return all EQ-5D-5L to EQ-5D-3L DSU value sets without references.valuesets(type="DSU",version="5L",references=NULL)#> Version Type Country#> 1 EQ-5D-5L DSU China#> 2 EQ-5D-5L DSU Germany#> 3 EQ-5D-5L DSU Japan#> 4 EQ-5D-5L DSU Netherlands#> 5 EQ-5D-5L DSU SouthKorea#> 6 EQ-5D-5L DSU Spain#> 7 EQ-5D-5L DSU UK
A number of methods have been published that enable the analysis ofEQ-5D profiles, most recently in the open access book Methods forAnalysing and Reporting EQ-5D Data byDevlin, Janssen andParkin. Theeq5d package includes R implentations of some of the methods from thisbook and from other sources that may be of use in analysing EQ-5D data.
Theeq5dcf function calculates the frequency, percentage,cumulative frequency and cumulative percentage for each five digitprofile in an EQ-5D dataset. Either a vector of five digit profiles or adata.frame of individual dimensions can be passed to this function inorder to summarise data in this way.
library(readxl)#load example datadata<- read_excel(system.file("extdata","eq5d3l_example.xlsx",package="eq5d"))#run eq5dcf function on a data.frameres<- eq5dcf(data,"3L")# Return data.frame of cumulative frequency stats (top 6 returned for brevity).head(res)#> State Frequency Percentage CumulativeFreq CumulativePerc#> 1 11121 36 18.0 36 18.0#> 2 11111 24 12.0 60 30.0#> 3 22222 21 10.5 81 40.5#> 4 22221 18 9.0 99 49.5#> 5 11221 12 6.0 111 55.5#> 6 21221 11 5.5 122 61.0
The eq5d package includes methods for summarising the severity of EQ-5Dhealth state. The Level Sum Score (LSS) treats each dimension’s level asa number rather than a category. Each number is added up to produce ascore between 5 and 15 for EQ-5D-3L and 5 and 25 for EQ-5D-5L.
lss(c(MO=1,SC=2,UA=3,PD=2,AD=1),version="3L")#> [1] 9lss(55555,version="5L")#> [1] 25lss(c(11111,12345,55555),version="5L")#> [1] 5 15 25
The Level Frequency Score (LFS) is an alternative method of summarisingprofile data developed byOppe and deCharro. Here the frequency ofthe levels for each health state are characterised. As described inDevlin, Janssen and Parkin’s book, the full health profile 11111 forEQ-5D-5L has 5, 1 s, no level 2, 3, 4 and 5s, so the LFS is 50000; thehealth profile 55555 is 00005; profiles such as 31524 and 53412 would be11111.
lfs(c(MO=1,SC=2,UA=3,PD=2,AD=1),version="3L")#> [1] "221"lfs(55555,version="5L")#> [1] "00005"lfs(c(11111,12345,55555),version="5L")#> [1] "50000" "11111" "00005"
The Paretian Classification of Health Change (PCHC) was developed byDevlin et al in 2010 and isused to compare changes in individuals over time. PCHC classifies thechange in an individual’s health state as better (improvement in atleast one dimension), worse (a deterioration in at least one dimension),mixed (improvements and deteriorations in dimensions) or there being nochange in the health state. Those classified in the No change group withthe 11111 health state can be separated into their own “No problems”group. PCHC can be calculated using thepchc function.
library(readxl)#load example datadata<- read_excel(system.file("extdata","eq5d3l_example.xlsx",package="eq5d"))#use first 50 entries of each group as pre/postpre<-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 totalsres1<- pchc(pre,post,version="3L",no.problems=FALSE,totals=FALSE)res1#> Number Percent#> No change 5 10#> Improve 32 64#> Worsen 10 20#> Mixed change 3 6#Show totals, but not those with no problemsres2<- pchc(pre,post,version="3L",no.problems=FALSE,totals=TRUE)res2#> Number Percent#> No change 5 10#> Improve 32 64#> Worsen 10 20#> Mixed change 3 6#> Total 50 100#Show totals and no problems for each dimensionres3<- pchc(pre,post,version="3L",no.problems=TRUE,totals=TRUE,by.dimension=TRUE)res3#> $MO#> Number Percent#> No change 16 57.1#> Improve 11 39.3#> Worsen 1 3.6#> Total with problems 28 56.0#> No problems 22 44.0#>#> $SC#> Number Percent#> No change 10 37.0#> Improve 14 51.9#> Worsen 3 11.1#> Total with problems 27 54.0#> No problems 23 46.0#>#> $UA#> Number Percent#> No change 10 25#> Improve 26 65#> Worsen 4 10#> Total with problems 40 80#> No problems 10 20#>#> $PD#> Number Percent#> No change 27 57.4#> Improve 19 40.4#> Worsen 1 2.1#> Total with problems 47 94.0#> No problems 3 6.0#>#> $AD#> Number Percent#> No change 7 30.4#> Improve 9 39.1#> Worsen 7 30.4#> Total with problems 23 46.0#> No problems 27 54.0#Don't summarise. Return all classificationsres4<- pchc(pre,post,version="3L",no.problems=TRUE,totals=FALSE,summary=FALSE)head(res4)#> [1] "Improve" "Improve" "Improve" "Improve" "Improve"#> [6] "Mixed change"
The Probability of Superiority (PS) is a non-parametric measure ofeffect size introduced byBuchholz etal in 2015 and enables theassessment of paired samples of EQ-5D profile data in the context ofassessing changes in health in terms of improvement or deterioration.For each EQ-5D dimension the number of subjects that have improved overtime is divided by the total number of matched pairs. Ties (those withno changes) were accounted for through the addition of half the numberof ties to the numerator. The score is less than 0.5 if more patientsdeteriorate than improve, 0.5 if the same number of patients improve anddeteriorate or do not change and greater than 0.5 if more patientsimprove than deteriorate.
library(readxl)#load example datadata<- read_excel(system.file("extdata","eq5d3l_example.xlsx",package="eq5d"))#use first 50 entries of each group as pre/postpre<-data[data$Group=="Group1",][1:50,]post<-data[data$Group=="Group2",][1:50,]res<- ps(pre,post,version="3L")res#> $MO#> [1] 0.6#>#> $SC#> [1] 0.61#>#> $UA#> [1] 0.72#>#> $PD#> [1] 0.68#>#> $AD#> [1] 0.52
The Health Profile Grid (HPG) was also introduced byDevlin etal in 2010. The HPG providesa visual way to observe changes in individuals between two time points.The HPG requires profiles for each time point to be ordered from best toworst. Thehpg function uses a specified value set for this withprofiles being assigned a ranking between 1 and 243 for EQ-5D-3L and 1and 3125 for EQ-5D-5L based on severity (1 being the best and 243/3125the worst). The rankings for the two time points for each individual areplotted with the location of each point showing whether there has beenan improvement or deterioration. The further a point is above the 45°line, the great the improvement in an individuals health. Conversely,the further below the line a point is the more health has deteriorated.Those individuals on the line show “no change”.
library(readxl)#load example datadata<- read_excel(system.file("extdata","eq5d3l_example.xlsx",package="eq5d"))#use first 50 entries of each group as pre/postpre<-data[data$Group=="Group1",][1:50,]post<-data[data$Group=="Group2",][1:50,]#run hpg function on data.frames#Show pre/post rankings and PCHC classificationres<- hpg(pre,post,country="UK",version="3L",type="TTO")head(res)#> Pre Post PCHC#> 1 11 8 Improve#> 2 161 8 Improve#> 3 23 1 Improve#> 4 20 1 Improve#> 5 23 9 Improve#> 6 33 97 Mixed change#Plot data using ggplot2library(ggplot2)ggplot(res, aes(Post,Pre,color=PCHC))+ geom_point(aes(shape=PCHC))+ coord_cartesian(xlim=c(1,243),ylim=c(1,243))+ scale_x_continuous(breaks=c(1,243))+ scale_y_continuous(breaks=c(1,243))+ annotate("segment",x=1,y=1,xend=243,yend=243,colour="black")+ theme(panel.border=element_blank(),panel.grid.minor=element_blank())+ xlab("Post-treatment")+ ylab("Pre-treatment")
Shannon’s indices were first used to assess how evenly EQ-5D dimensionscores or health states in a dataset are distributed byJanssen etal in 2007. Shannon’s H’(diversity) index represents the absolute amount of informativitycaptured with Shannon’s J’ (evenness) index capturing the evenness ofthe distribution of data. Shannon’s J’ is calculated by dividing H’ byH’ max to give a value between 0 and 1. Lower values indicate morediversity and higher values indicate less.
library(readxl)#load example datadata<- read_excel(system.file("extdata","eq5d3l_example.xlsx",package="eq5d"))#Shannon's H', H' max and J' for the whole datasetshannon(data,version="3L",by.dimension=FALSE)#> $H#> [1] 4.17#>#> $H.max#> [1] 7.92#>#> $J#> [1] 0.53#Shannon's H', H' max and J' for each dimensionres<- shannon(data,version="3L",by.dimension=TRUE)#Convert to data.frame for ease of viewingdo.call(rbind,res)#> H H.max J#> MO 1 1.58 0.63#> SC 0.97 1.58 0.61#> UA 1.22 1.58 0.77#> PD 1.13 1.58 0.71#> AD 1.09 1.58 0.69
The Health State Density Curve (HSDC) was introduced byZamora etalin 2018 and provides a graphical way to depict the distribution of EQ-5Dprofiles. The cumulative frequency of health profiles ranked from mostto least frequent is plotted against the cumulative proportion of thedistinct health profiles (red line) and can be compared a uniformdistribution representing total equality (black line). The Health StateDensity Index (HSDI) is based on the area formed by the diagonal linerepresenting total equality and the line of the HSDC. The HSDI has avalue between 0 and 1 where a value of 0 represents total inequality and1 total equality.
#load example datadata<- read_excel(system.file("extdata","eq5d3l_example.xlsx",package="eq5d"))#Calculate HSDIhsdi<- hsdi(data,version="3L")#Plot HSDCcf<- eq5dcf(data,version="3L",proportions=T)cf$CumulativeState<-1:nrow(cf)/nrow(cf)#Plot data using ggplot2library(ggplot2)ggplot(cf, aes(CumulativeProp,CumulativeState))+ geom_line(color="#FF9999")+ annotate("segment",x=0,y=0,xend=1,yend=1,colour="black")+ annotate("text",x=0.5,y=0.9,label=paste0("HSDI=",hsdi))+ theme(panel.border=element_blank(),panel.grid.minor=element_blank())+ coord_cartesian(xlim=c(0,1),ylim=c(0,1))+ xlab("Cumulative proportion of observations")+ ylab("Cumulative proportion of profiles")
Theeq5dds function is an R approximation of the Stata commandwritten byRamos-Goñi &Ramallo-Fariña.The function analyses and summarises the descriptive components of anEQ-5D dataset. The “by” argument enables a grouping variable to bespecified when analysing the data subgroup.
set.seed(12345)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")#> MO SC UA PD AD#> 1 8.3 33.3 33.3 41.7 16.7#> 2 58.3 50.0 25.0 16.7 33.3#> 3 33.3 16.7 41.7 41.7 50.0eq5dds(dat,version="3L",counts=TRUE)#> MO SC UA PD AD#> 1 1 4 4 5 2#> 2 7 6 3 2 4#> 3 4 2 5 5 6eq5dds(dat,version="3L",by="Sex")#> data[, by]: Female#> MO SC UA PD AD#> 1 0.0 33.3 33.3 33.3 0.0#> 2 66.7 50.0 33.3 16.7 16.7#> 3 33.3 16.7 33.3 50.0 83.3#> ------------------------------------------------------------#> data[, by]: Male#> MO SC UA PD AD#> 1 16.7 33.3 33.3 50.0 33.3#> 2 50.0 50.0 16.7 16.7 50.0#> 3 33.3 16.7 50.0 33.3 16.7
Helper functions are included, which may be useful in the processing ofEQ-5D data.get_all_health_states returns a vector of all possiblefive digit health states for a specified EQ-5D version.get_dimensions_from_health_states splits a vector of five digithealth states into a data.frame of their individual components andget_health_states_from_dimensions combines indiviual dimensions ina data.frame into five digit health states.
# Get all EQ-5D-3L five digit health states (top 6 returned for brevity).head(get_all_health_states("3L"))#> [1] "11111" "11112" "11113" "11121" "11122" "11123"# Split five digit health states into their individual components.get_dimensions_from_health_states(c("12345","54321"),version="5L")#> MO SC UA PD AD#> 1 1 2 3 4 5#> 2 5 4 3 2 1
Example data is included with the package and can be accessed using thesystem.file function.
# View example files.dir(path=system.file("extdata",package="eq5d"))#> [1] "eq5d3l_example.csv" "eq5d3l_example.xlsx"#> [3] "eq5d3l_five_digit_example.csv" "eq5d3l_five_digit_example.xlsx"#> [5] "eq5d5l_example.csv" "eq5d5l_example.xlsx"# Read example EQ-5D-3L data.library(readxl)data<- read_excel(system.file("extdata","eq5d3l_example.xlsx",package="eq5d"))# Calculate index scoresscores<- eq5d(data,country="UK",version="3L",type="TTO")# Top 6 scoreshead(scores)#> [1] 0.760 0.796 -0.003 0.796 0.656 1.000
The calculation (and visualisation) of multiple EQ-5D indices can alsobe performed by upload of a CSV or Excel file using the packagedShiny app. This requires theshiny,DT,FSA,ggplot2,ggiraph,ggiraphExtra,mime,PMCMRplus,readxl,shinycssloadersandshinyWidgetspackages. Ideally the CSV/Excel headers should be the same as the namesof the vector passed to theeq5d function i.e. MO, SC, UA, PD andAD or the column name “State” if using the five digit format. However, amodal dialog will prompt the user to select the appropriate columns ifthe defaults can not be found. Both files below will produce the sameresults.
The app is launched using theshiny_eq5d function.
shiny_eq5d()
Alternatively, it can be accessed without installing R/Shiny/eq5d byvisitingshinyapps.io.
This project is licensed under the MIT License - see theLICENSE.md filefor details.
About
Resources
License
Unknown, MIT licenses found
Licenses found
Uh oh!
There was an error while loading.Please reload this page.
Stars
Watchers
Forks
Packages0
Uh oh!
There was an error while loading.Please reload this page.
Contributors5
Uh oh!
There was an error while loading.Please reload this page.