extras/CodeUsedToPopulate2.R
In ohdsi-studies/PCE: A Package Skeleton for Replicating Published Prediction Models
replaceName(packageLocation = getwd(),
packageName = 'PCE')
source("C:/Users/admin_jreps/Documents/SetBaseUrl.R")
#baseUrl <- Sys.getenv('baseUrl')
populatePackageCohorts(targetCohortIds = c(1325,1322,1326,1328,
1358,1359,1360,1361),
targetCohortNames = c('Black Male Persons who are statin-risk eligible',
'Black Female Persons who are statin-risk eligible',
'Non-Black Male Persons who are statin-risk eligible',
'Non-Black Female Persons who are statin-risk eligible',
'Black Male Persons who are statin-risk eligible not censored at statin initiation',
'Black Female Persons who are statin-risk eligible not censored at statin initiation',
'Non-Black Male Persons who are statin-risk eligible not censored at statin initiation',
'Non-Black Female Persons who are statin-risk eligible not censored at statin initiation'),
outcomeIds = c(1466,1357),#,1365,1366),
outcomeNames = c('first of AMI or ischemic stroke or death IP required',
'first of AMI or ischemic stroke or death no IP required'
#'first occurrence of atheroclerotic cardiovascular disease',
#'first occurrence of atheroclerotic cardiovascular disease no IP required'
),
baseUrl = baseUrl)
# 1295 - hypertensive drugs
# 1286 - smoker
# 1362 - diabetes
populatePackageModels(modelname = 'pooled_male_non_black',
standardCovariates = NULL,
cohortCovariateSettings = list(baseUrl = baseUrl,
atlasCovariateIds = c(1362,1286,1286),
atlasCovariateNames = c('diabetes', 'smoking','smoking'),
analysisIds = c(456,456,455),
startDays = c(-9999,-730,-730),
endDays = c(-1,60,60),
points = c(0.658,7.837,-1.795),
count = rep(F, 3),
ageInteraction = c(F,F,F),
lnAgeInteraction = c(F,F,T)) ,
measurementCovariateSettings = list(names = c('Total_Cholesterol_mgdL', 'Total_Cholesterol_mgdL_age',
'HDL-C_mgdL', 'HDL-C_mgdL_age'
),
conceptSets = list(c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902)
),
startDays = c(-1825, -1825,-1825, -1825),
endDays = c(0,0,0,0),
scaleMaps= list(function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)}),
points = c(11.853,-2.664,-7.990,1.769),
aggregateMethods = c('recent','recent','recent', 'recent'),
imputationValues = c(150,150,50,50),
ageInteractions = c(F,F,F,F),
lnAgeInteractions = c(F,T,F,T),
lnValues = c(T,T,T,T),
measurementIds = c(1,2,3,4),
analysisIds = c(457,457,457,457)
),
ageCovariateSettings = list(names = c('log(age)'),
ageMaps = list(function(x){return(log(x))}),
ageIds = 1,
analysisIds = c(458),
points = c(12.344)
),
measurementCohortCovariateSettings = list(names = c('treated_Systolic_BP_mm_Hg','untreated_Systolic_BP_mm_Hg'),
atlasCovariateIds = c(1295,1295),
types = c('in', 'out'),
conceptSets = list(c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778)
),
startDays = c(-365,-365),
endDays = c(0,0),
scaleMaps= list(function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)}
),
points = c(1.797,1.764),
aggregateMethods = c('recent','recent'),
imputationValues = c(120,120),
ageInteractions = c(F,F),
lnAgeInteractions = c(F,F),
lnValues = c(T,T),
measurementIds = c(1,2),
analysisIds = c(459,459),
baseUrl = baseUrl
),
finalMapping = function(x){ 1- 0.9144^exp(x-61.18)}
)
populatePackageModels(modelname = 'pooled_male_black',
standardCovariates = NULL,
cohortCovariateSettings = list(baseUrl = baseUrl,
atlasCovariateIds = c(1362,1286),
atlasCovariateNames = c('diabetes', 'smoking'),
analysisIds = c(456,456),
startDays = c(-9999,-730),
endDays = c(-1,60),
points = c(0.645,0.549),
count = rep(F, 2),
ageInteraction = c(F,F),
lnAgeInteraction = c(F,F)) ,
measurementCovariateSettings = list(names = c('Total_Cholesterol_mgdL',
'HDL-C_mgdL_age'
),
conceptSets = list(c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902)
),
startDays = c(-1825, -1825),
endDays = c(0,0),
scaleMaps= list(function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)}),
points = c(0.302,-0.307),
aggregateMethods = c('recent','recent'),
imputationValues = c(150,50),
ageInteractions = c(F,F),
lnAgeInteractions = c(F,F),
lnValues = c(T,T),
measurementIds = c(1,2),
analysisIds = c(457,457)
),
ageCovariateSettings = list(names = c('log(age)'),
ageMaps = list(function(x){return(log(x))}),
ageIds = 1,
analysisIds = c(458),
points = c(2.469)
),
measurementCohortCovariateSettings = list(names = c('treated_Systolic_BP_mm_Hg','untreated_Systolic_BP_mm_Hg'),
atlasCovariateIds = c(1295,1295),
types = c('in', 'out'),
conceptSets = list(c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778)
),
startDays = c(-365,-365),
endDays = c(0,0),
scaleMaps= list(function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)}
),
points = c(1.916,1.809),
aggregateMethods = c('recent','recent'),
imputationValues = c(120,120),
ageInteractions = c(F,F),
lnAgeInteractions = c(F,F),
lnValues = c(T,T),
measurementIds = c(1,2),
analysisIds = c(459,459),
baseUrl = baseUrl
),
finalMapping = function(x){ 1- 0.8954^exp(x-19.54)}
)
populatePackageModels(modelname = 'pooled_female_non_black',
standardCovariates = NULL,
cohortCovariateSettings = list(baseUrl = baseUrl,
atlasCovariateIds = c(1362,1286 ,1286 ),
atlasCovariateNames = c('diabetes', 'smoking','smoking'),
analysisIds = c(456,456,455),
startDays = c(-9999,-730,-730),
endDays = c(-1,60,60),
points = c(0.661,7.574,-1.665),
count = rep(F, 3),
ageInteraction = c(F,F,F),
lnAgeInteraction = c(F,F,T)) ,
measurementCovariateSettings = list(names = c('Total_Cholesterol_mgdL', 'Total_Cholesterol_mgdL_age',
'HDL-C_mgdL', 'HDL-C_mgdL_age'
),
conceptSets = list(c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902)
),
startDays = c(-1825, -1825,-1825, -1825),
endDays = c(0,0,0,0),
scaleMaps= list(function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)}),
points = c(13.540,-3.114,-13.578,3.149),
aggregateMethods = c('recent','recent','recent', 'recent'),
imputationValues = c(150,150,50,50),
ageInteractions = c(F,F,F,F),
lnAgeInteractions = c(F,T,F,T),
lnValues = c(T,T,T,T),
measurementIds = c(1,2,3,4),
analysisIds = c(457,457,457,457)
),
ageCovariateSettings = list(names = c('log(age)', 'log(age)_squared'),
ageMaps = list(function(x){return(log(x))},
function(x){return(log(x)^2)}),
ageIds = c(1,2),
analysisIds = c(458,458),
points = c(-29.799,4.884)
),
measurementCohortCovariateSettings = list(names = c('treated_Systolic_BP_mm_Hg','untreated_Systolic_BP_mm_Hg'),
atlasCovariateIds = c(1295,1295),
types = c('in', 'out'),
conceptSets = list(c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778)
),
startDays = c(-365,-365),
endDays = c(0,0),
scaleMaps= list(function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)}
),
points = c(2.019,1.957),
aggregateMethods = c('recent','recent'),
imputationValues = c(120,120),
ageInteractions = c(F,F),
lnAgeInteractions = c(F,F),
lnValues = c(T,T),
measurementIds = c(1,2),
analysisIds = c(459,459),
baseUrl = baseUrl
),
finalMapping = function(x){ 1- 0.9665^exp(x+29.18)}
)
populatePackageModels(modelname = 'pooled_female_black',
standardCovariates = NULL,
cohortCovariateSettings = list(baseUrl = baseUrl,
atlasCovariateIds = c(1362,1286 ),
atlasCovariateNames = c('diabetes', 'smoking'),
analysisIds = c(456,456),
startDays = c(-9999,-730),
endDays = c(-1,60),
points = c(0.874,0.691),
count = rep(F, 2),
ageInteraction = c(F,F),
lnAgeInteraction = c(F,F)) ,
measurementCovariateSettings = list(names = c('Total_Cholesterol_mgdL',
'HDL-C_mgdL', 'HDL-C_mgdL_age'
),
conceptSets = list(c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902)
),
startDays = c(-1825,-1825, -1825),
endDays = c(0,0,0),
scaleMaps= list(function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)}),
points = c(0.940,-18.920,4.475),
aggregateMethods = c('recent','recent','recent'),
imputationValues = c(150,50,50),
ageInteractions = c(F,F,F),
lnAgeInteractions = c(F,F,T),
lnValues = c(T,T,T),
measurementIds = c(1,2,3),
analysisIds = c(457,457,457)
),
ageCovariateSettings = list(names = c('log(age)'),
ageMaps = list(function(x){return(log(x))}),
ageIds = c(1),
analysisIds = c(458),
points = c(17.114)
),
measurementCohortCovariateSettings = list(names = c('treated_Systolic_BP_mm_Hg','untreated_Systolic_BP_mm_Hg',
'treated_Systolic_BP_mm_Hg','untreated_Systolic_BP_mm_Hg'),
atlasCovariateIds = c(1295,1295,1295,1295),
types = c('in', 'out'),
conceptSets = list(c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778)
),
startDays = c(-365,-365,-365,-365),
endDays = c(0,0,0,0),
scaleMaps= list(function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)}
),
points = c(29.291,27.820,-6.432,-6.087),
aggregateMethods = c('recent','recent','recent','recent'),
imputationValues = c(120,120,120,120),
ageInteractions = c(F,F,F,F),
lnAgeInteractions = c(F,F,T,T),
lnValues = c(T,T,T,T),
measurementIds = c(1,2,3,4),
analysisIds = c(459,459,459,459),
baseUrl = baseUrl
),
finalMapping = function(x){ 1- 0.9533^exp(x-86.61)}
)
ohdsi-studies/PCE documentation built on Feb. 15, 2021, 6:33 a.m.
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replaceName(packageLocation = getwd(),
packageName = 'PCE')
source("C:/Users/admin_jreps/Documents/SetBaseUrl.R")
#baseUrl <- Sys.getenv('baseUrl')
populatePackageCohorts(targetCohortIds = c(1325,1322,1326,1328,
1358,1359,1360,1361),
targetCohortNames = c('Black Male Persons who are statin-risk eligible',
'Black Female Persons who are statin-risk eligible',
'Non-Black Male Persons who are statin-risk eligible',
'Non-Black Female Persons who are statin-risk eligible',
'Black Male Persons who are statin-risk eligible not censored at statin initiation',
'Black Female Persons who are statin-risk eligible not censored at statin initiation',
'Non-Black Male Persons who are statin-risk eligible not censored at statin initiation',
'Non-Black Female Persons who are statin-risk eligible not censored at statin initiation'),
outcomeIds = c(1466,1357),#,1365,1366),
outcomeNames = c('first of AMI or ischemic stroke or death IP required',
'first of AMI or ischemic stroke or death no IP required'
#'first occurrence of atheroclerotic cardiovascular disease',
#'first occurrence of atheroclerotic cardiovascular disease no IP required'
),
baseUrl = baseUrl)
# 1295 - hypertensive drugs
# 1286 - smoker
# 1362 - diabetes
populatePackageModels(modelname = 'pooled_male_non_black',
standardCovariates = NULL,
cohortCovariateSettings = list(baseUrl = baseUrl,
atlasCovariateIds = c(1362,1286,1286),
atlasCovariateNames = c('diabetes', 'smoking','smoking'),
analysisIds = c(456,456,455),
startDays = c(-9999,-730,-730),
endDays = c(-1,60,60),
points = c(0.658,7.837,-1.795),
count = rep(F, 3),
ageInteraction = c(F,F,F),
lnAgeInteraction = c(F,F,T)) ,
measurementCovariateSettings = list(names = c('Total_Cholesterol_mgdL', 'Total_Cholesterol_mgdL_age',
'HDL-C_mgdL', 'HDL-C_mgdL_age'
),
conceptSets = list(c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902)
),
startDays = c(-1825, -1825,-1825, -1825),
endDays = c(0,0,0,0),
scaleMaps= list(function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)}),
points = c(11.853,-2.664,-7.990,1.769),
aggregateMethods = c('recent','recent','recent', 'recent'),
imputationValues = c(150,150,50,50),
ageInteractions = c(F,F,F,F),
lnAgeInteractions = c(F,T,F,T),
lnValues = c(T,T,T,T),
measurementIds = c(1,2,3,4),
analysisIds = c(457,457,457,457)
),
ageCovariateSettings = list(names = c('log(age)'),
ageMaps = list(function(x){return(log(x))}),
ageIds = 1,
analysisIds = c(458),
points = c(12.344)
),
measurementCohortCovariateSettings = list(names = c('treated_Systolic_BP_mm_Hg','untreated_Systolic_BP_mm_Hg'),
atlasCovariateIds = c(1295,1295),
types = c('in', 'out'),
conceptSets = list(c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778)
),
startDays = c(-365,-365),
endDays = c(0,0),
scaleMaps= list(function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)}
),
points = c(1.797,1.764),
aggregateMethods = c('recent','recent'),
imputationValues = c(120,120),
ageInteractions = c(F,F),
lnAgeInteractions = c(F,F),
lnValues = c(T,T),
measurementIds = c(1,2),
analysisIds = c(459,459),
baseUrl = baseUrl
),
finalMapping = function(x){ 1- 0.9144^exp(x-61.18)}
)
populatePackageModels(modelname = 'pooled_male_black',
standardCovariates = NULL,
cohortCovariateSettings = list(baseUrl = baseUrl,
atlasCovariateIds = c(1362,1286),
atlasCovariateNames = c('diabetes', 'smoking'),
analysisIds = c(456,456),
startDays = c(-9999,-730),
endDays = c(-1,60),
points = c(0.645,0.549),
count = rep(F, 2),
ageInteraction = c(F,F),
lnAgeInteraction = c(F,F)) ,
measurementCovariateSettings = list(names = c('Total_Cholesterol_mgdL',
'HDL-C_mgdL_age'
),
conceptSets = list(c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902)
),
startDays = c(-1825, -1825),
endDays = c(0,0),
scaleMaps= list(function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)}),
points = c(0.302,-0.307),
aggregateMethods = c('recent','recent'),
imputationValues = c(150,50),
ageInteractions = c(F,F),
lnAgeInteractions = c(F,F),
lnValues = c(T,T),
measurementIds = c(1,2),
analysisIds = c(457,457)
),
ageCovariateSettings = list(names = c('log(age)'),
ageMaps = list(function(x){return(log(x))}),
ageIds = 1,
analysisIds = c(458),
points = c(2.469)
),
measurementCohortCovariateSettings = list(names = c('treated_Systolic_BP_mm_Hg','untreated_Systolic_BP_mm_Hg'),
atlasCovariateIds = c(1295,1295),
types = c('in', 'out'),
conceptSets = list(c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778)
),
startDays = c(-365,-365),
endDays = c(0,0),
scaleMaps= list(function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)}
),
points = c(1.916,1.809),
aggregateMethods = c('recent','recent'),
imputationValues = c(120,120),
ageInteractions = c(F,F),
lnAgeInteractions = c(F,F),
lnValues = c(T,T),
measurementIds = c(1,2),
analysisIds = c(459,459),
baseUrl = baseUrl
),
finalMapping = function(x){ 1- 0.8954^exp(x-19.54)}
)
populatePackageModels(modelname = 'pooled_female_non_black',
standardCovariates = NULL,
cohortCovariateSettings = list(baseUrl = baseUrl,
atlasCovariateIds = c(1362,1286 ,1286 ),
atlasCovariateNames = c('diabetes', 'smoking','smoking'),
analysisIds = c(456,456,455),
startDays = c(-9999,-730,-730),
endDays = c(-1,60,60),
points = c(0.661,7.574,-1.665),
count = rep(F, 3),
ageInteraction = c(F,F,F),
lnAgeInteraction = c(F,F,T)) ,
measurementCovariateSettings = list(names = c('Total_Cholesterol_mgdL', 'Total_Cholesterol_mgdL_age',
'HDL-C_mgdL', 'HDL-C_mgdL_age'
),
conceptSets = list(c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902)
),
startDays = c(-1825, -1825,-1825, -1825),
endDays = c(0,0,0,0),
scaleMaps= list(function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)}),
points = c(13.540,-3.114,-13.578,3.149),
aggregateMethods = c('recent','recent','recent', 'recent'),
imputationValues = c(150,150,50,50),
ageInteractions = c(F,F,F,F),
lnAgeInteractions = c(F,T,F,T),
lnValues = c(T,T,T,T),
measurementIds = c(1,2,3,4),
analysisIds = c(457,457,457,457)
),
ageCovariateSettings = list(names = c('log(age)', 'log(age)_squared'),
ageMaps = list(function(x){return(log(x))},
function(x){return(log(x)^2)}),
ageIds = c(1,2),
analysisIds = c(458,458),
points = c(-29.799,4.884)
),
measurementCohortCovariateSettings = list(names = c('treated_Systolic_BP_mm_Hg','untreated_Systolic_BP_mm_Hg'),
atlasCovariateIds = c(1295,1295),
types = c('in', 'out'),
conceptSets = list(c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778)
),
startDays = c(-365,-365),
endDays = c(0,0),
scaleMaps= list(function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)}
),
points = c(2.019,1.957),
aggregateMethods = c('recent','recent'),
imputationValues = c(120,120),
ageInteractions = c(F,F),
lnAgeInteractions = c(F,F),
lnValues = c(T,T),
measurementIds = c(1,2),
analysisIds = c(459,459),
baseUrl = baseUrl
),
finalMapping = function(x){ 1- 0.9665^exp(x+29.18)}
)
populatePackageModels(modelname = 'pooled_female_black',
standardCovariates = NULL,
cohortCovariateSettings = list(baseUrl = baseUrl,
atlasCovariateIds = c(1362,1286 ),
atlasCovariateNames = c('diabetes', 'smoking'),
analysisIds = c(456,456),
startDays = c(-9999,-730),
endDays = c(-1,60),
points = c(0.874,0.691),
count = rep(F, 2),
ageInteraction = c(F,F),
lnAgeInteraction = c(F,F)) ,
measurementCovariateSettings = list(names = c('Total_Cholesterol_mgdL',
'HDL-C_mgdL', 'HDL-C_mgdL_age'
),
conceptSets = list(c(2212267,3015232,3019900,3027114,4008265,4190897,4198448,4260765,37393449,37397989,40484105,44791053,44809580),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902),
c(4076704,2212449,2212449,3003767,3007070,3007676,3011884,3013473,3015204,3016945,3022449,3023602,3023752,3024401,3030792,3032771,3033638,3034482,3040815,3053286,4005504,4008127,4011133,4019543,4041557,4041720,4042059,4042081,4055665,4076704,4101713,4195503,4198116,37208659,37208661,37392562,37392938,37394092,37394229,37394230,37398699,40757503,40765014,44789188,45768617,45768651,45768652,45768653,45768654,45771001,45772902)
),
startDays = c(-1825,-1825, -1825),
endDays = c(0,0,0),
scaleMaps= list(function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 130 & rawValue <= 320 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)); return(x)},
function(x){ x = dplyr::mutate(x, rawValue = dplyr::case_when(unitConceptId == 8753 ~ rawValue*38.6, unitConceptId %in% c(8840,8954,9028 ) ~ rawValue, TRUE ~ 0)); x= dplyr::filter(x, rawValue >= 20 & rawValue <= 100 ); x = dplyr::mutate(x,valueAsNumber = log(rawValue)*log(age)); return(x)}),
points = c(0.940,-18.920,4.475),
aggregateMethods = c('recent','recent','recent'),
imputationValues = c(150,50,50),
ageInteractions = c(F,F,F),
lnAgeInteractions = c(F,F,T),
lnValues = c(T,T,T),
measurementIds = c(1,2,3),
analysisIds = c(457,457,457)
),
ageCovariateSettings = list(names = c('log(age)'),
ageMaps = list(function(x){return(log(x))}),
ageIds = c(1),
analysisIds = c(458),
points = c(17.114)
),
measurementCohortCovariateSettings = list(names = c('treated_Systolic_BP_mm_Hg','untreated_Systolic_BP_mm_Hg',
'treated_Systolic_BP_mm_Hg','untreated_Systolic_BP_mm_Hg'),
atlasCovariateIds = c(1295,1295,1295,1295),
types = c('in', 'out'),
conceptSets = list(c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778),
c(3004249,3009395,3018586,3028737,3035856,4152194,4153323,4161413,4197167,4217013,4232915,4248525,4292062,21492239,37396683,44789315,44806887,45769778)
),
startDays = c(-365,-365,-365,-365),
endDays = c(0,0,0,0),
scaleMaps= list(function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)},
function(x){ x = dplyr::filter(x, rawValue >= 90 & rawValue <= 200 ); return(x)}
),
points = c(29.291,27.820,-6.432,-6.087),
aggregateMethods = c('recent','recent','recent','recent'),
imputationValues = c(120,120,120,120),
ageInteractions = c(F,F,F,F),
lnAgeInteractions = c(F,F,T,T),
lnValues = c(T,T,T,T),
measurementIds = c(1,2,3,4),
analysisIds = c(459,459,459,459),
baseUrl = baseUrl
),
finalMapping = function(x){ 1- 0.9533^exp(x-86.61)}
)
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