Function to correct implausible ordering in Australian valueset for EQ-5D-3L

Description

Correcting the implausible ordering

Usage

.correctImplausibleOrdering(scores)

Arguments

Value

the value that read from the stored dataframe

Examples

.correctImplausibleOrdering(11121)

EQ-5D-3L index values (for each set of response of 3L) for different countries

Description

EQ-5D-3L index values (for each set of response of 3L) for different countries

Usage

EQ5D3L_indexvalues.df

Format

A 243 by 38 dataframe

Note

: For testing purpose -not required by users

: VAS value for state 3333 was reported as -0.022, rather obtained -0.034 and needs to be checked with authors

: There were some implausible orderings and hard coded those only for Australian value sets

Source

Argentina: TTO - Appendix A in Augustovski et al (2009) <doi:10.1111/j.1524-4733.2008.00468.x>

Argentina: VAS - Appendix A in Augustovski et al (2009) <doi:10.1111/j.1524-4733.2008.00468.x>

Australia: Supplementary in Viney et al (2011) <doi:10.1016/j.jval.2011.04.009>

Belgium: VAS - Selected example page 209 in Cleemput et al (2010) <doi:10.1007/s10198-009-0167-0>

Brazil: Appendix 1 in Santos et al (2016) <doi:10.1177/0272989X15613521>

Canada: Supplementary material Table S2 in Bansback et al (2012) <https://doi.org/10.1371/journal.pone.0031115>

Chile: Table 4 page 1139 in Zarate et al (2011) <doi:10.1016/j.jval.2011.09.002

China: Supplementary materials Appendix 2 in Liu et al (2014) <doi:10.1016/j.jval.2014.05.007>

Denmark: TTO - Appendix in Wittrup-Jensen et al (2009) <doi:10.1177/1403494809105287>

Denmark: VAS - Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

Europe: VAS - Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

Finland: VAS - Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

France: Selected example page 61 in Chevalier et al (2013) <doi:10.1007/s10198-011-0351-x>

Germany: TTO - Selected examples Table 6 page 130 in Greiner et al (2005) <doi:10.1007/s10198-004-0264-z>

Germany: VAS - Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

Iran: Selected example page 173 in Goudarzi et al (2019) <doi:10.1016/j.vhri.2019.01.007>

Italy: Supplementary materials Appendix 2 in Scalone et al (2013) <http://dx.doi.org/10.1016/j.jval.2013.04.008>

Japan: Tsuchiya et al (2002) <https://doi.org/10.1002/hec.673>

Korea: Selected example page 1191 in Lee et al <doi:10.1111/j.1524-4733.2009.00579.x>

Malaysia: VAS - Supplementary material Appendix 3 in Yusof et al (2019) <doi:10.1016/j.jval.2011.11.024>

Netherlands: Lamers et al <doi:10.1002/hec.1124>

New Zealand: VAS - Selected examples Table 7 column 5 page 542 in Devlin et al <doi:10.1002/hec.741>

Poland: Table 6 page 294 in Golicki et al <https://doi.org/10.1111/j.1524-4733.2009.00596.x>

Portugal: Supplementary Material 1 in Ferreira et al <doi:10.1007/s11136-013-0448-z>

Slovenia: VAS - Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

Singapore: Selected examples in Nan Luo et al <doi:10.1007/s40273-014-0142-1>

Spain: TTO- Badia et al (2001) <doi:10.1177/0272989X0102100102>

Spain: VAS - Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

Sri Lanka: Selected example page 1789 in Kularatna et al (2015) <doi:10.1007/s11136-014-0906-2>

Sweden: Supplementary Table 3 in Burström et al (2014) <doi:10.1007/s11136-013-0496-4>

Taiwan: Table 3 page 703 in Lee et al (2013) <http://dx.doi.org/10.1016/j.jfma.2012.12.015> #'

Thailand: Tongsiri et al (2011) <doi:10.1016/j.jval.2011.06.005>

Trinidad and Tobago: Table 5 page 66 in Bailey et al (2016) <http://dx.doi.org/10.1016/j.vhri.2016.07.010>

UK : TTO - Selected examples Table 3 page 1105 in Dolan et al (1997) <http://dx.doi.org/10.1097/00005650-199711000-00002>

UK: VAS - Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

USA: Appendix 1 page 218 in Shaw et al (2005) <doi:10.1097/00005650-200503000-00003>

Zimbabwe: Jelsma et al (2003) <https://doi.org/10.1186/1478-7954-1-11>

EQ-5D-3L tariffs using TTO for different countries

Description

EQ-5D-3L tariffs using TTO for different countries

Usage

EQ5D3L_tariffs_TTO.df

Format

A 38 by 28 dataframe

Source

Argentina: Table 3 column 5 page 560 in Augustovski et al (2009) <doi:10.1111/j.1524-4733.2008.00468.x>

Australia: Table 4 column 6 page 933 in Viney et al (2011) <doi:10.1016/j.jval.2011.04.009>

Brazil: Table 2 column 8 page 21 in Santos et al (2016) <doi:10.1177/0272989X15613521>

Canada: Table 4 column 2 page 8 in Bansback et al (2012) <https://doi.org/10.1371/journal.pone.0031115>

Chile: Table 2 column 5 page 1137 in Zarate et al (2011) <doi:10.1016/j.jval.2011.09.002

China: Table 4 column 4 page 603 in Liu et al (2014) <doi:10.1016/j.jval.2014.05.007>

Denmark: Table 4 column 2 page 463 in Wittrup-Jensen et al (2009) <doi:10.1177/1403494809105287>

France: Equation page 61 in Chevalier et al (2013) <doi:10.1007/s10198-011-0351-x>

Germany: Table 4 column 2 page 129 in Greiner et al (2005) <doi:10.1007/s10198-004-0264-z>

Hungary: Table 2 column 11 page 1238 in Rencz et al (2020) <doi:10.1016/j.jval.2020.03.019>

Iran: Table 3 column 8 page 174 in Goudarzi et al (2019) <doi:10.1016/j.vhri.2019.01.007>

Italy: Table 4 column 5 page 820 in Scalone et al (2013) <http://dx.doi.org/10.1016/j.jval.2013.04.008>

Japan: Table 4 column 1 page 41 in Tsuchiya et al (2002) <https://doi.org/10.1002/hec.673>

South Korea: Table 3 column 4 page 1191 in Lee et al <doi:10.1111/j.1524-4733.2009.00579.x>

Malaysia: Table 4 column 5 page 588 in Aryani et al <doi:10.1016/j.jval.2011.11.024>

Netherlands: Table 5 column 3 page 1128 in Lamers et al <doi:10.1002/hec.1124>

Poland: Table 5 column 2 page 293 in Golicki et al <https://doi.org/10.1111/j.1524-4733.2009.00596.x>

Portugal: Table 4 column 6 page 418 in Ferreira et al <doi:10.1007/s11136-013-0448-z>

Singapore: Equation page 504 in Nan Luoß et al <doi:10.1007/s40273-014-0142-1>

Spain: Table 3 column 4 page 13 in Badia et al (2001) <doi:10.1177/0272989X0102100102>

Sri Lanka: Table 2 column 8 page 1791 in Kularatna et al (2015) <doi:10.1007/s11136-014-0906-2>

Sweden: Table 2 column 8 page 436 in Burström et al (2014) <doi:10.1007/s11136-013-0496-4>

Taiwan: Table 2 column 4 page 702 in Lee et al (2013) <http://dx.doi.org/10.1016/j.jfma.2012.12.015> #'

Thailand: Table 1 column 2 page 1144 (parameters like MO3 are calculated) Tongsiri et al (2011) <doi:10.1016/j.jval.2011.06.005>

Trinidad and Tobago: Table 4 page 65 in Bailey et al (2016) <http://dx.doi.org/10.1016/j.vhri.2016.07.010>

UK: Table 1 column 2 page 1103 in Dolan et al (1997) <http://dx.doi.org/10.1097/00005650-199711000-00002>

USA: Table 5 column 2 page 214 in Shaw et al (2005) <doi:10.1097/00005650-200503000-00003>

Zimbabwe: Table 5 column 3 page 7 in Jelsma et al (2003) <https://doi.org/10.1186/1478-7954-1-11>

EQ-5D-3L tariffs using VAS for different countries

Description

EQ-5D-3L tariffs using VAS for different countries

Usage

EQ5D3L_tariffs_VAS.df

Format

A 34 by 12 dataframe

Source

Argentina: Table 3 column 2 page 560 in Augustovski et al (2009) <doi:10.1111/j.1524-4733.2008.00468.x>

Belgium: Equation 2 page 208 in Cleemput et al (2010) <doi:10.1007/s10198-009-0167-0>

Denmark: Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

Europe: Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

Finland: Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

Germany: Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

Malaysia: Table 4 column 6 page S88 in Yusof et al (2019) <doi:10.1016/j.jval.2011.11.024>

New Zealand: Equation 2 page 541 in Devlin et al <doi:10.1002/hec.741>

Slovenia: Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

Spain: Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1> (this is not shown in euroqol website) Could not get Sweden VAS values

UK: Table 2.3 page 14 in Szende et al (2014) <doi:10.1007/978-94-007-7596-1>

EQ-5D-5L crosswalk value sets for 10 countries

Description

EQ-5D-5L crosswalk value sets for 10 countries

Usage

EQ5D5L_crosswalk_indexvalues.df

Format

A 3125 by 11 dataframe

Note

: For testing purpose -not required by users

Source

https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/valuation-standard-value-sets/crosswalk-index-value-calculator/ (accessed on Aug 03,2019)

Van Hout et al (2012) <doi: 10.1016/j.jval.2012.02.008>.

EQ-5D-5L index values

Description

EQ-5D-5L index values

Usage

EQ5D5L_indexvalues.df

Format

A 3125 by 22 dataframe

Note

: For testing purpose -not required by users

Source

Canada: Selected example Table A3 in Xie et al (2016) <doi:10.1097/MLR.0000000000000447>

China: Supplementary Material 1 in Luo et al (2017) <doi:10.1016/j.jval.2016.11.016>

England: Selected example Table 3 page 18 and supporting information in Devlin et al (2018) <doi:10.1002/hec.3564>

Ethopia: Table 3 column 8 page 12 and supporting information in Welie et al (2019) <doi:10.1016/j.vhri.2019.08.475>

France: Table 3 column 2-6 page 12 and supporting information in Andrade et al (2020) <doi::10.1007/s40273-019-00876-4>

Germany: Supplementary Material 1 in Ludwig et al (2018) <doi:10.1007/s40273-018-0615-8>

Hong Kong: Selected examples Table 3 page 244 in Wong et al (2018) <doi:10.1007/s40271-017-0278-0>

Indonesia: Selected examples page 1162 in Purba et al (2017) <doi:10.1007/s40273-017-0538-9>

Ireland: Selected example Table 2 page 1348 in Hobbins et al (2016) <doi:10.1007/s40273-018-0690-x>

Japan: Shiroiwa, et al (2016) <doi:10.1016/j.jval.2016.03.1834>

Korea: Selected example page 1848 in Kim et al (2016) <doi:10.1007/s11136-015-1205-2>

Malaysia: Shafie et al (2019) <doi:10.1007/s40273-018-0758-7>

Netherlands: Versteegh et al (2016) <doi:10.1016/j.jval.2016.01.003>

Poland: Supplementary Material 3 in Golicki et al <doi:10.1007/s40273-019-00811-7>

Portugal: Ferreira1 et al (2014) <doi:10.1007/s11136-019-02226-5>

Spain: Selected examples in Table 1 in Ramos-Goñiet et al (2018) <https://doi.org/10.1016/j.jval.2017.10.023>

Taiwan: Table 3 page 10 in Lin et al (2018) <https://doi.org/10.1371/journal.pone.0209344>

Thailand: Pattanaphesaj et al (2018) <doi:10.1080/14737167.2018>

Uruguay: Augustovski et al (2016) <doi:10.1007/s11136-015-1086-4>

USA: Pickard et al (2019) <doi:10.1016/j.jval.2019.02.009>

Vietnam: Mai et al (2020) <doi:10.1007/s11136-020-02469-7>

EQ-5D-5L tariffs for different countries

Description

EQ-5D-5L tariffs for different countries

Usage

EQ5D5L_tariffs.df

Format

A 34 by 22 data frame

Source

Canada: Table 2 column 5 page 103 in Xie et al (2016) <doi:10.1097/MLR.0000000000000447>

China: Table 4 column 4 page 667 in Luo et al (2017) <doi:10.1016/j.jval.2016.11.016>

England: Table 2 column 2 page 17 in Devlin et al (2018) <doi:10.1002/hec.3564>

Ethiopia: Table 3 column 8 page 12 in Welie et al (2019) <doi:10.1016/j.vhri.2019.08.475>

France: Table 3 column 2-6 page 12 in Andrade et al (2019) <doi::10.1007/s40273-019-00876-4>

Germany: Table column 9 page 670 in Ludwig et al (2018) <doi:10.1007/s40273-018-0615-8>

Hong Kong: Table 3 column 8 page 244 in Wong et al (2018) <doi:10.1007/s40271-017-0278-0>

Hungary: Table 3 column 14 page 1241 in Rencz et al (2020) <doi:10.1016/j.jval.2020.03.019>

Indonesia: Table 3 column 8 page 1162 in Purba et al (2017) <doi:10.1007/s40273-017-0538-9>

Ireland: Table 2 column 2 page 1348 in Hobbins et al (2016) <doi:10.1007/s40273-018-0690-x>

Japan: Table 2 column 7 page 651 in Shiroiwa, et al (2016) <doi:10.1016/j.jval.2016.03.1834>

Korea: Table 5 column 6 page 1851 in Kim et al (2016) <doi:10.1007/s11136-015-1205-2>

Malaysia: Table 2 column 9 page 720 in Shafie et al (2019) <doi:10.1007/s40273-018-0758-7>

Netherlands: Table 4 column 8 page 350 in Versteegh et al (2016) <doi:10.1016/j.jval.2016.01.003>

Poland: Table 2 column 7 in Golicki et al <doi:10.1007/s40273-019-00811-7>

Portugal: Table 3 column 4 in Ferreira1 et al (2014) <doi:10.1007/s11136-019-02226-5>

Spain: Table 1 column 9 page 5 in Ramos-Goñiet et al (2018) <https://doi.org/10.1016/j.jval.2017.10.023>

Taiwan: Table 2 column 4 page 9 in Lin et al (2018) <https://doi.org/10.1371/journal.pone.0209344>

Thailand: Table 3 column 6 page 4 in Pattanaphesaj et al (2018) <doi:10.1080/14737167.2018>

Uruguay: Table 2.3 column 5 page 29 in Augustovski et al (2016) <doi:10.1007/s11136-015-1086-4>

USA: Table 2 column 2 page 939 in Pickard et al (2019) <doi:10.1016/j.jval.2019.02.009>

Vietnam: Table 3 column 5 in Mai et al (2020) <doi:10.1007/s11136-020-02469-7>

Probability matrix for the cross walk

Description

Probability matrix for the cross walk

Usage

Probability_matrix_crosswalk.df

Format

A dataframe with 3124 rows and 243 columns

Source

https://euroqol.org/wp-content/uploads/2018/02/EQ-5D-5L_Crosswalk_model_and__methodology2.pdf

Van Hout et al (2012) <doi: 10.1016/j.jval.2012.02.008>.

Function to check the given column exists

Description

Function to check the given column exists

Usage

check_column_exist(column_name, data)

Arguments

Value

0 if success -1 if failure

Examples

check_column_exist("age", data.frame(
  age = rep(20, 4), sex = rep("male", 4),
  stringsAsFactors = FALSE
))

Function to check the EQ-5D-3L scores

Description

Function to check the EQ-5D-3L scores

Usage

check_scores_3L(dimen, dimen2 = NA, dimen3 = NA, dimen4 = NA, dimen5 = NA)

Arguments

Examples

check_scores_3L(c(1, 2, 3, 3, 3))
check_scores_3L(1, 2, 3, 3, 3)
check_scores_3L(1, 2, 3, 2, 3)
check_scores_3L(12323)

Function to check the EQ-5D-5L scores

Description

Function to check the EQ-5D-5L scores

Usage

check_scores_5L(dimen, dimen2 = NA, dimen3 = NA, dimen4 = NA, dimen5 = NA)

Arguments

Examples

check_scores_5L(c(1, 2, 3, 5, 3))
check_scores_5L(1, 2, 3, 4, 3)
check_scores_5L(12323)

Function to convert a number to individual digits

Description

Function to convert a number to individual digits

Usage

convert_number_to_digits(this_number)

Arguments

Value

digits

Examples

convert_number_to_digits(234)

Function to return descriptive statistics, sum, no of observations, mean, mode. median, range, standard deviation and standard error

Description

Function to return descriptive statistics, sum, no of observations, mean, mode. median, range, standard deviation and standard error

Usage

descriptive_stat_data_column(colum, column_name, nrcode = NA)

Arguments

Value

the descriptive statistics for success , -1 for failure

Examples

descriptive_stat_data_column(c(1, 2, 3, 4, NA), "scores", NA)

Function to return the column number for a given column name (from list of possible column names that may have used) in a data frame

Description

Function to return the column number for a given column name (from list of possible column names that may have used) in a data frame

Usage

get_colno_existing_colnames(column_names, data)

Arguments

Value

the column number

Examples

get_colno_existing_colnames(c("age"), data.frame(age = rep(20, 4), 
gender = rep("male", 4)))

Function to return the column number for column name

Description

Function to return the column number for column name

Usage

get_column_no_colnames(data, column_name)

Arguments

Value

column number, if success -1, if failure

Examples

get_column_no_colnames(data.frame(age = rep(20, 4), 
sex = rep("male", 4)), "sex")

Function to return frequency table

Description

Function to return frequency table

Usage

get_frequency_table(v)

Arguments

Value

frequency table

Examples

get_frequency_table(c(1, 1, 1, 12, 2))

Function to return mode

Description

Function to return mode

Usage

get_mode_for_vec(v)

Arguments

Value

mode if success -1 for failure

Examples

get_mode_for_vec(c(1, 1, 2, 3))

Function to map EQ-5D-5L scores to EQ-5D-3L index values as per the specific country and group by gender and age

Description

Function to map EQ-5D-5L scores to EQ-5D-3L index values

Usage

map_5Lto3L(
  eq5dresponse_data,
  mobility,
  self_care,
  usual_activities,
  pain_discomfort,
  anxiety,
  country = "UK",
  method = "CW",
  groupby = NULL,
  agelimit = NULL
)

Arguments

Value

index value if success, negative values for failure

Examples

map_5Lto3L(data.frame(
  mo = c(1), sc = c(4), ua = c(4), pd = c(3),
  ad = c(3)
), "mo", "sc", "ua", "pd", "ad")

Function to map EQ-5D-5L descriptive system to 3L index value

Description

Function to map EQ-5D-5L descriptive system to 3L index value (ref:Van Hout et al 2012 and code inspired from https://github.com/brechtdv/eq5d-mapping)

Usage

map_5Lto3L_Ind(
  country = "UK",
  method = "CW",
  dimen,
  dimen2 = NA,
  dimen3 = NA,
  dimen4 = NA,
  dimen5 = NA
)

Arguments

Value

index value of EQ-5D-3L, -1 if any error

Examples

map_5Lto3L_Ind("UK", "CW", 11125)
map_5Lto3L_Ind("UK", "CW", c(1, 1, 1, 2, 5))
map_5Lto3L_Ind("UK", "CW", 1, 1, 1, 2, 5)

Function to add an underscore for texts with spaces in between

Description

Function to add an underscore for texts with spaces in between

Usage

replace_space_underscore(this_string)

Arguments

Value

string where the spaces replaced by "_"

Examples

replace_space_underscore("Sri Lanka")

Function to check the gender column and age column subset based on the values in it have used) in a data frame

Description

Function to check the gender column and age column subset based on the values in it have used) in a data frame

Usage

subset_gender_age_to_group(data, gender, agelimit)

Arguments

Value

the column number

Examples

subset_gender_age_to_group(data.frame(age = rep(20, 4), gender = 
rep("male", 4)), "male", c(10, 70))

Function to check format of a numeric column when the values are not bounded

Description

Function to check format of a numeric column when the values are not bounded

Usage

test_data_num_norange(vec, nrcode = NA)

Arguments

Value

0, if success -1, if failure

Examples

test_data_num_norange(c(1, 2, 3, 4, -99), -99)

Function to throw error on invalid directory or file or if the file is not readable

Description

Function to throw error on invalid directory or file or if the file is not readable

Usage

test_file_exist_read(filename)

Arguments

Value

0 if success, non zero negative values if failure

Examples

test_file_exist_read(system.file("extdata", "blank.txt", 
package = "valueEQ5D"))

Function to value EQ-5D-3L columns to index values for any country and group by gender and age

Description

Main function to value EQ-5D-5L descriptive system to 5L index values.

Usage

value_3L(
  eq5dresponse_data,
  mo,
  sc,
  ua,
  pd,
  ad,
  country,
  method,
  groupby,
  agelimit
)

Arguments

Value

the descriptive statistics of index values, frequency table and the modified data where the last column will be the index values data<-data.frame(age=c(10,20),sex=c("M","F"),mo=c(1,2),sc=c(1,2),ua=c(3,4), pd=c(3,1),ad=c(3,1)) value_3L(data, "mo", "sc","ua", "pd", "ad","UK","TTO",NULL,c(10,70))

Function to value EQ-5D-3L scores for various countries

Description

Function to value EQ-5D-3L scores for various countries

Usage

value_3L_Ind(
  country,
  method,
  dimen,
  dimen2 = NA,
  dimen3 = NA,
  dimen4 = NA,
  dimen5 = NA
)

Arguments

Value

index value based if success, negative values for failure

Examples

value_3L_Ind("UK", "TTO", 23131)
value_3L_Ind("Spain", "TTO", 2, 3, 1, 3, 1)
value_3L_Ind("Denmark", "VAS", c(1, 2, 3, 1, 3))

Function to value EQ-5D-5L scores for any country and group by gender and age

Description

Function to value EQ-5D-5L descriptive system to index value.

Usage

value_5L(
  eq5dresponse_data,
  mo,
  sc,
  ua,
  pd,
  ad,
  country = "England",
  groupby = NULL,
  agelimit = NULL
)

Arguments

Value

index value if success, negative values for failure

Examples

data <- data.frame(
  age = c(10, 20), sex = c("M", "F"),
  mo = c(1, 2), sc = c(1, 2), ua = c(3, 4), pd = c(3, 4), ad = c(3, 4)
)
value_5L(data, "mo", "sc", "ua", "pd", "ad", "England", NULL, c(10, 70))

Function to value EQ-5D-5L scores for various countries

Description

Function to value EQ-5D-5L scores for various countries

Usage

value_5L_Ind(
  country,
  dimen,
  dimen2 = NA,
  dimen3 = NA,
  dimen4 = NA,
  dimen5 = NA
)

Arguments

Value

index values if success, negative values if failure

Examples

value_5L_Ind("England", 23434)
value_5L_Ind("China", 2, 3, 4, 3, 4)
value_5L_Ind("Poland", c(1, 2, 3, 4, 3))