Author: Sheeja Manchira Krishnan
Date: 2021-10-06

# User Guide

#### Sheeja Manchira Krishnan

#### 2021-10-06

## valueEQ5D

EQ-5D is a standardized instrument developed by the EuroQol(R) Group as a measure of health-related quality of life that can be used in a wide range of health conditions and treatments (https://euroqol.org/eq-5d-instruments/). The EQ-5D consists of a descriptive system and a visual analog scale (VAS).

The descriptive system comprises five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. The EQ-5D VAS records the patients’ self-rated health on a vertical visual analogue scale. This can be used as a quantitative measure of health outcome that reflects the patients’ own judgment. The scores on these five dimensions can be presented as a health profile or can be converted to a single summary index number (utility) reflecting preferably compared to other health profiles.

Currently three versions of EQ-5D exist:

EQ-5D with 3 levels of severity for each of the 5 dimensions: EQ-5D-3L EQ-5D with 5 levels of severity for each of the 5 dimensions: EQ-5D-5L EQ-5D for use in children: EQ-5D-Y

This package can be used for valuing the adult EQ-5D descriptive system scores - both 5L and 3L for different countries. EQ-5D-5L scores can be valued for the following countries: Canada, China, England, Ethiopia, France, Germany, Hong Kong, Hungary, Indonesia, Ireland, Japan, Korea, Malaysia, Netherlands, Poland, Portugal, Spain, Taiwan, Thailand, Uruguay, USA and Vietnam.

Canada: Xie et al (2016) :10.1097/MLR.0000000000000447 China:Luo et al (2017) :10.1016/j.jval.2016.11.016 England: Devlin et al (2018) :10.1002/hec.3564 Ethiopia: Welie et al (2019) :10.1016/j.vhri.2019.08.475 France:Andrade et al (2019) ::10.1007/s40273-019-00876-4 Germany: Ludwig et al (2018) :10.1007/s40273-018-0615-8 Hong Kong: Wong et al (2018) :10.1007/s40271-017-0278-0 Hungary:Rencz et al (2020) :10.1016/j.jval.2020.03.019 Indonesia: Purba et al (2017) :10.1007/s40273-017-0538-9 Ireland: Hobbins et al (2016) :10.1007/s40273-018-0690-x Japan: Shiroiwa, et al (2016) :10.1016/j.jval.2016.03.1834 Korea: Kim et al (2016) :10.1007/s11136-015-1205-2 Malaysia: Shafie et al (2019) :10.1007/s40273-018-0758-7 Netherlands: Versteegh et al (2016) :10.1016/j.jval.2016.01.003 Poland: Golicki et al :10.1007/s40273-019-00811-7 Portugal:Ferreira1 et al (2014) :10.1007/s11136-019-02226-5 Spain: Ramos-Goñiet et al (2018) ://doi.org/10.1016/j.jval.2017.10.023 Taiwan: Lin et al (2018) ://doi.org/10.1371/journal.pone.0209344 Thailand: Pattanaphesaj et al (2018) :10.1080/14737167.2018 Uruguay: Augustovski et al (2016) :10.1007/s11136-015-1086-4 USA: Pickard et al (2019) :10.1016/j.jval.2019.02.009 Vietnam: Mai et al (2020) :10.1007/s11136-020-02469-7

EQ-5D-3L scores can be valued for the countries Argentina, Australia, Belgium, Brazil, Canada, Chile, China, Denmark, Europe, Finland, France, Germany, Hungary, Iran, Italy, Japan, Korea, Malaysia, Netherlands, New Zealand, Poland, Portugal, Singapore, Slovenia, Spain, Sri Lanka, Sweden, Taiwan, Thailand, Trinidad and Tobago, UK, USA, and Zimbabwe.

Argentina: Augustovski et al (2009) :10.1111/j.1524-4733.2008.00468.x Australia: Viney et al (2011) :10.1016/j.jval.2011.04.009 Belgium: Cleemput et al (2010) :10.1007/s10198-009-0167-0 Brazil: Santos et al (2016) :10.1177/0272989X15613521 Canada: Bansback et al (2012) ://doi.org/10.1371/journal.pone.0031115 Chile: Zarate et al (2011) :10.1016/j.jval.2011.09.002. China: Liu et al (2014) :10.1016/j.jval.2014.05.007 Denmark TTO: Wittrup-Jensen et al (2009) :10.1177/1403494809105287 Denmark VAS: Szende et al (2014) :10.1007/978-94-007-7596-1 Europe: Szende et al (2014) :10.1007/978-94-007-7596-1 Finland: Szende et al (2014) :10.1007/978-94-007-7596-1 France: Chevalier et al (2013) :10.1007/s10198-011-0351-x Germany (TTO): Greiner et al (2005) :10.1007/s10198-004-0264-z Germany (VAS): Szende et al (2014) :10.1007/978-94-007-7596-1 Hungary (TTO): Rencz et al (2020) :10.1016/j.jval.2020.03.019 Iran: Goudarzi et al (2019) :10.1016/j.vhri.2019.01.007 Italy: Scalone et al (2013) ://dx.doi.org/10.1016/j.jval.2013.04.008 Japan: Tsuchiya et al (2002) ://doi.org/10.1002/hec.673 Korea: Lee et al :10.1111/j.1524-4733.2009.00579.x Malaysia: Yusof et al (2019) :10.1016/j.jval.2011.11.024 Netherlands: Lamers et al :10.1002/hec.1124 New Zealand: Devlin et al :10.1002/hec.741 Poland: Golicki et al ://doi.org/10.1111/j.1524-4733.2009.00596.x Portugal: Ferreira et al :10.1007/s11136-013-0448-z Singapore: Nan Luo et al :10.1007/s40273-014-0142-1 Slovenia: Szende et al (2014) :10.1007/978-94-007-7596-1 Spain (TTO): Badia et al (2001) :10.1177/0272989X0102100102 Spain (VAS): Szende et al (2014) :10.1007/978-94-007-7596-1 Sri Lanka: Kularatna et al (2015) :10.1007/s11136-014-0906-2 Sweden: Burström et al (2014) :10.1007/s11136-013-0496-4 Taiwan: Lee et al (2013) ://dx.doi.org/10.1016/j.jfma.2012.12.015 Thailand: Tongsiri et al (2011) :10.1016/j.jval.2011.06.005 Trinidad and Tobago: Bailey et al (2016) ://dx.doi.org/10.1016/j.vhri.2016.07.010 UK (TTO): Dolan et al (1997) ://dx.doi.org/10.1097/00005650-199711000-00002 UK (VAS): Szende et al (2014) :10.1007/978-94-007-7596-1 USA: Shaw et al (2005) :10.1097/00005650-200503000-00003 Zimbabwe: Jelsma et al (2003) ://doi.org/10.1186/1478-7954-1-11

The 5L descriptive scores can be mapped to 3L index values for 10 countries using the NICE recommended Van Hout et al. method.

If the individual responses are in column formats (e.g. in csv) they can be used as arguments in the methods.

In brief, for valuing EQ-5D-3L responses from individual responses to the descriptive system, use “value_3LIindscores”;for valuing EQ-5D-5L responses from descriptive system, use “value_5LIindscores”; and for mapping EQ-5D-5L responses from descriptive system to EQ-5D-3L index values, use “map_5Lto3L_Ind”. The arguments for all these three parameters will be country names and followed by the five individual responses.

If the requirement is to get the summary statistics of collected EQ-5D responses from many individuals with conditions on gender and age use these methods: valuing EQ-5D-3L responses use “value_3L”;for valuing EQ-5D-5L responses, use “value_5L”; and for mapping EQ-5D-5L responses to EQ-5D-3L index values, use “map_5Lto3L”. The arguments for all these three parameters will be country names and followed by the five column names of the EQ-5D responses and the data containing these EQ-5D responses.

EQ-5D-5L responses for England are converted to index values using Devlin et al. method. EQ-5D-3L responses for England are converted to index values using Dolan et al. method. EQ-5D-5L responses for England are mapped to EQ-5D-3L index values using Van Hout et al. method.

Whenever the EQ-5D-5L responses are taken as input parameters, code checks if the input values are with in the bounds, i.e for 3L they have to be between 1 and 3 and for 5L between 1 and 5, throws error otherwise.

## Data

For demonstration purposes, a simulated data set representing treatment and control arm of randomised controlled trial will be used. If any of the responses are invalid i.e other than 1 to 5 for EQ-5D-5L or 1 to 3 for EQ-5D-3L, it will throw error and return -1.

## Examples- Valuing EQ-5D-3L

Each of the below calls will give same answer while valuing the EQ-5D-3L individual score 1, 2, 3, 2, 2 for mobility, self care, social activity, pain and discomfort, and anxiety respectively.

    #> [1] 0.258
    #> [1] 0.309
    #> [1] 0.258

When the data is in column format as in example below, use the ‘value3L’ to get the summary statistics while returning back the modified data. Use conditions if the results need to be based on a particular gender or particular age group as in the examples below. This will provide the summary statistics, frequency table, histogram and modified data

The results can be called using the stats, frequencyTable, histogram and modifiedData which are given below.

    #>            Sum   Mean      SD Median  Mode        SE Minimum Maximum Count
    #> EQ-5D-3L 2.502 0.2502 0.38686   0.25 0.436 0.1223359  -0.371   0.725    10
    #> NULL
    #> $breaks
    #> [1] -0.4 -0.2  0.0  0.2  0.4  0.6  0.8
    #>
    #> $counts
    #> [1] 2 0 3 1 1 3
    #>
    #> $density
    #> [1] 1.0 0.0 1.5 0.5 0.5 1.5
    #>
    #> $mids
    #> [1] -0.3 -0.1  0.1  0.3  0.5  0.7
    #>
    #> $xname
    #> [1] "scores_noNA"
    #>
    #> $equidist
    #> [1] TRUE
    #>
    #> attr(,"class")
    #> [1] "histogram"
    #> NULL

Similarly, we can use the options to get the results for particular gender with given age ranges.

## Examples- Valuing EQ-5D-5L

Similarly, each of the below calls values EQ-5D-5L individual score 1, 2, 3, 4, 5 for mobility, self care, social activity, pain and discomfort, and anxiety respectively. For EQ-5D-5L, no method to be given explicitly.

    #> [1] 0.322
    #> [1] 0.322
    #> [1] 0.322
    #> [1] 0.141
    #> [1] 0.309
    #> [1] 0.24

When the data is in column format as in example below, use the ‘value_5L’ to get the summary statistics while returning back the modified data. Use conditions if the results need to be based on a particular gender or particular age group as in the examples below. This will provide the summary statistics, frequency table, histogram and modified data

    #> $stats
    #>            Sum   Mean        SD Median  Mode         SE Minimum Maximum Count
    #> EQ-5D-5L 2.805 0.2805 0.2848739 0.2075 0.026 0.09008505  -0.112   0.667    10
    #>
    #> $freq_table
    #>                    scores               Freq Cumul relative
    #> -0.112             "-0.112"             "1"  "1"   "0.1"
    #> 0.026              "0.026"              "1"  "2"   "0.1"
    #> 0.0519999999999999 "0.0519999999999999" "1"  "3"   "0.1"
    #> 0.085              "0.085"              "1"  "4"   "0.1"
    #> 0.087              "0.087"              "1"  "5"   "0.1"
    #> 0.328              "0.328"              "1"  "6"   "0.1"
    #> 0.529              "0.529"              "1"  "7"   "0.1"
    #> 0.55               "0.55"               "1"  "8"   "0.1"
    #> 0.593              "0.593"              "1"  "9"   "0.1"
    #> 0.667              "0.667"              "1"  "10"  "0.1"
    #>
    #> $histogram
    #> $breaks
    #> [1] -0.2  0.0  0.2  0.4  0.6  0.8
    #>
    #> $counts
    #> [1] 1 4 1 3 1
    #>
    #> $density
    #> [1] 0.5 2.0 0.5 1.5 0.5
    #>
    #> $mids
    #> [1] -0.1  0.1  0.3  0.5  0.7
    #>
    #> $xname
    #> [1] "scores_noNA"
    #>
    #> $equidist
    #> [1] TRUE
    #>
    #> attr(,"class")
    #> [1] "histogram"
    #>
    #> $modified_data
    #>         age sex          arm eq5d5L.q1 eq5d5L.q2 eq5d5L.q3 eq5d5L.q4 eq5d5L.q5
    #> 1  39.69983   F      Control         4         5         5         4         3
    #> 2  58.40727   M Intervention         5         2         3         3         1
    #> 3  55.34026   F      Control         2         2         3         3         2
    #> 4  43.65464   M Intervention         5         4         2         5         5
    #> 5  75.44182   F Intervention         1         5         5         1         4
    #> 6  56.68776   M Intervention         5         3         1         4         4
    #> 7  79.45749   M      Control         3         4         1         2         3
    #> 8  94.33068   F      Control         3         2         4         3         2
    #> 9  65.10474   M      Control         1         4         5         4         5
    #> 10 67.33162   F      Control         5         1         2         5         5
    #>    EQ-5D-5L scores
    #> 1            0.026
    #> 2            0.529
    #> 3            0.667
    #> 4           -0.112
    #> 5            0.328
    #> 6            0.085
    #> 7            0.593
    #> 8            0.550
    #> 9            0.087
    #> 10           0.052

    #> $stats
    #>            Sum    Mean        SD Median  Mode        SE Minimum Maximum Count
    #> EQ-5D-5L 0.589 0.14725 0.2710773  0.086 0.529 0.1355387  -0.112   0.529     4
    #>
    #> $freq_table
    #>        scores   Freq Cumul relative
    #> -0.112 "-0.112" "1"  "1"   "0.25"
    #> 0.085  "0.085"  "1"  "2"   "0.25"
    #> 0.087  "0.087"  "1"  "3"   "0.25"
    #> 0.529  "0.529"  "1"  "4"   "0.25"
    #>
    #> $histogram
    #> $breaks
    #> [1] -0.2  0.0  0.2  0.4  0.6
    #>
    #> $counts
    #> [1] 1 2 0 1
    #>
    #> $density
    #> [1] 1.25 2.50 0.00 1.25
    #>
    #> $mids
    #> [1] -0.1  0.1  0.3  0.5
    #>
    #> $xname
    #> [1] "scores_noNA"
    #>
    #> $equidist
    #> [1] TRUE
    #>
    #> attr(,"class")
    #> [1] "histogram"
    #>
    #> $modified_data
    #>        age sex          arm eq5d5L.q1 eq5d5L.q2 eq5d5L.q3 eq5d5L.q4 eq5d5L.q5
    #> 2 58.40727   M Intervention         5         2         3         3         1
    #> 4 43.65464   M Intervention         5         4         2         5         5
    #> 6 56.68776   M Intervention         5         3         1         4         4
    #> 9 65.10474   M      Control         1         4         5         4         5
    #>   EQ-5D-5L scores
    #> 2           0.529
    #> 4          -0.112
    #> 6           0.085
    #> 9           0.087

    #> $stats
    #>             Sum    Mean        SD Median  Mode        SE Minimum Maximum Count
    #> EQ-5D-5L -0.441 -0.0882 0.3082664 -0.136 0.035 0.1378609  -0.502    0.34     5
    #>
    #> $freq_table
    #>        scores   Freq Cumul relative
    #> -0.502 "-0.502" "1"  "1"   "0.2"
    #> -0.178 "-0.178" "1"  "2"   "0.2"
    #> -0.136 "-0.136" "1"  "3"   "0.2"
    #> 0.035  "0.035"  "1"  "4"   "0.2"
    #> 0.34   "0.34"   "1"  "5"   "0.2"
    #>
    #> $histogram
    #> $breaks
    #> [1] -0.6 -0.4 -0.2  0.0  0.2  0.4
    #>
    #> $counts
    #> [1] 1 0 2 1 1
    #>
    #> $density
    #> [1] 1 0 2 1 1
    #>
    #> $mids
    #> [1] -0.5 -0.3 -0.1  0.1  0.3
    #>
    #> $xname
    #> [1] "scores_noNA"
    #>
    #> $equidist
    #> [1] TRUE
    #>
    #> attr(,"class")
    #> [1] "histogram"
    #>
    #> $modified_data
    #>        age sex          arm eq5d5L.q1 eq5d5L.q2 eq5d5L.q3 eq5d5L.q4 eq5d5L.q5
    #> 2 58.40727   M Intervention         5         2         3         3         1
    #> 4 43.65464   M Intervention         5         4         2         5         5
    #> 6 56.68776   M Intervention         5         3         1         4         4
    #> 7 79.45749   M      Control         3         4         1         2         3
    #> 9 65.10474   M      Control         1         4         5         4         5
    #>   EQ-5D-5L scores
    #> 2           0.035
    #> 4          -0.502
    #> 6          -0.178
    #> 7           0.340
    #> 9          -0.136

    #> $stats
    #>             Sum       Mean        SD Median   Mode        SE Minimum Maximum
    #> EQ-5D-5L -1.293 -0.1847143 0.5225316  -0.34 -0.264 0.1974984  -0.778   0.637
    #>          Count
    #> EQ-5D-5L     7
    #>
    #> $freq_table
    #>        scores   Freq Cumul relative
    #> -0.778 "-0.778" "1"  "1"   "0.142857142857143"
    #> -0.549 "-0.549" "1"  "2"   "0.142857142857143"
    #> -0.435 "-0.435" "1"  "3"   "0.142857142857143"
    #> -0.34  "-0.34"  "1"  "4"   "0.142857142857143"
    #> -0.264 "-0.264" "1"  "5"   "0.142857142857143"
    #> 0.436  "0.436"  "1"  "6"   "0.142857142857143"
    #> 0.637  "0.637"  "1"  "7"   "0.142857142857143"
    #>
    #> $histogram
    #> $breaks
    #> [1] -1.0 -0.5  0.0  0.5  1.0
    #>
    #> $counts
    #> [1] 2 3 1 1
    #>
    #> $density
    #> [1] 0.5714286 0.8571429 0.2857143 0.2857143
    #>
    #> $mids
    #> [1] -0.75 -0.25  0.25  0.75
    #>
    #> $xname
    #> [1] "scores_noNA"
    #>
    #> $equidist
    #> [1] TRUE
    #>
    #> attr(,"class")
    #> [1] "histogram"
    #>
    #> $modified_data
    #>         age sex          arm eq5d5L.q1 eq5d5L.q2 eq5d5L.q3 eq5d5L.q4 eq5d5L.q5
    #> 1  39.69983   F      Control         4         5         5         4         3
    #> 2  58.40727   M Intervention         5         2         3         3         1
    #> 3  55.34026   F      Control         2         2         3         3         2
    #> 4  43.65464   M Intervention         5         4         2         5         5
    #> 6  56.68776   M Intervention         5         3         1         4         4
    #> 9  65.10474   M      Control         1         4         5         4         5
    #> 10 67.33162   F      Control         5         1         2         5         5
    #>    EQ-5D-5L scores
    #> 1           -0.264
    #> 2            0.436
    #> 3            0.637
    #> 4           -0.778
    #> 6           -0.340
    #> 9           -0.435
    #> 10          -0.549

## Examples- Mapping EQ-5D-5L scores to EQ-5D-3L index values for UK and other countries

Each of the below calls will give same EQ-5d-3L index values while valuing the EQ-5D-5L individual score 1, 2, 3, 4, 5 for mobility, self care, social activity, pain and discomfort, and anxiety respectively.

    #> [1] 0.06333624
    #> [1] 0.06333624
    #> [1] 0.2231107

When the data is in column format as in example below, use the ‘map_5Lto3L’ to get the summary statistics while returning back the modified data. Use conditions if the results need to be based on a particular gender or particular age group as in the examples below.

    #> $stats
    #>               Sum      Mean        SD     Median        Mode        SE
    #> EQ-5D-3L 0.774706 0.0774706 0.3415737 0.02580007 -0.04373611 0.1080151
    #>             Minimum   Maximum Count
    #> EQ-5D-3L -0.4456148 0.5603099    10
    #>
    #> $freq_table
    #>                 scores            Freq Cumul relative
    #> -0.445614751936 "-0.445614751936" "1"  "1"   "0.1"
    #> -0.314878640776 "-0.314878640776" "1"  "2"   "0.1"
    #> -0.148030571904 "-0.148030571904" "1"  "3"   "0.1"
    #> -0.043736108406 "-0.043736108406" "1"  "4"   "0.1"
    #> -0.038803950411 "-0.038803950411" "1"  "5"   "0.1"
    #> 0.090404081656  "0.090404081656"  "1"  "6"   "0.1"
    #> 0.19003726705   "0.19003726705"   "1"  "7"   "0.1"
    #> 0.440675890086  "0.440675890086"  "1"  "8"   "0.1"
    #> 0.484342829996  "0.484342829996"  "1"  "9"   "0.1"
    #> 0.560309909273  "0.560309909273"  "1"  "10"  "0.1"
    #>
    #> $histogram
    #> $breaks
    #> [1] -0.6 -0.4 -0.2  0.0  0.2  0.4  0.6
    #>
    #> $counts
    #> [1] 1 1 3 2 0 3
    #>
    #> $density
    #> [1] 0.5 0.5 1.5 1.0 0.0 1.5
    #>
    #> $mids
    #> [1] -0.5 -0.3 -0.1  0.1  0.3  0.5
    #>
    #> $xname
    #> [1] "scores"
    #>
    #> $equidist
    #> [1] TRUE
    #>
    #> attr(,"class")
    #> [1] "histogram"
    #>
    #> $modified_data
    #>         age sex          arm eq5d5L.q1 eq5d5L.q2 eq5d5L.q3 eq5d5L.q4 eq5d5L.q5
    #> 1  39.69983   F      Control         4         5         5         4         3
    #> 2  58.40727   M Intervention         5         2         3         3         1
    #> 3  55.34026   F      Control         2         2         3         3         2
    #> 4  43.65464   M Intervention         5         4         2         5         5
    #> 5  75.44182   F Intervention         1         5         5         1         4
    #> 6  56.68776   M Intervention         5         3         1         4         4
    #> 7  79.45749   M      Control         3         4         1         2         3
    #> 8  94.33068   F      Control         3         2         4         3         2
    #> 9  65.10474   M      Control         1         4         5         4         5
    #> 10 67.33162   F      Control         5         1         2         5         5
    #>    Mapped EQ-5D-3L scores
    #> 1             -0.04373611
    #> 2              0.09040408
    #> 3              0.56030991
    #> 4             -0.44561475
    #> 5              0.19003727
    #> 6             -0.14803057
    #> 7              0.48434283
    #> 8              0.44067589
    #> 9             -0.03880395
    #> 10            -0.31487864

    #> $stats
    #>                 Sum       Mean        SD      Median       Mode        SE
    #> EQ-5D-3L -0.5420452 -0.1355113 0.2285541 -0.09341726 0.09040408 0.1142771
    #>             Minimum    Maximum Count
    #> EQ-5D-3L -0.4456148 0.09040408     4
    #>
    #> $freq_table
    #>                 scores            Freq Cumul relative
    #> -0.445614751936 "-0.445614751936" "1"  "1"   "0.25"
    #> -0.148030571904 "-0.148030571904" "1"  "2"   "0.25"
    #> -0.038803950411 "-0.038803950411" "1"  "3"   "0.25"
    #> 0.090404081656  "0.090404081656"  "1"  "4"   "0.25"
    #>
    #> $histogram
    #> $breaks
    #> [1] -0.6 -0.4 -0.2  0.0  0.2
    #>
    #> $counts
    #> [1] 1 0 2 1
    #>
    #> $density
    #> [1] 1.25 0.00 2.50 1.25
    #>
    #> $mids
    #> [1] -0.5 -0.3 -0.1  0.1
    #>
    #> $xname
    #> [1] "scores"
    #>
    #> $equidist
    #> [1] TRUE
    #>
    #> attr(,"class")
    #> [1] "histogram"
    #>
    #> $modified_data
    #>        age sex          arm eq5d5L.q1 eq5d5L.q2 eq5d5L.q3 eq5d5L.q4 eq5d5L.q5
    #> 2 58.40727   M Intervention         5         2         3         3         1
    #> 4 43.65464   M Intervention         5         4         2         5         5
    #> 6 56.68776   M Intervention         5         3         1         4         4
    #> 9 65.10474   M      Control         1         4         5         4         5
    #>   Mapped EQ-5D-3L scores
    #> 2             0.09040408
    #> 4            -0.44561475
    #> 6            -0.14803057
    #> 9            -0.03880395