tests/testthat/test-predict_10yr_ascvd_risk.R
In bcjaeger/PooledCohort: Predicted Risk for CVD using Pooled Cohort Equations, PREVENT Equations, and Other Contemporary CVD Risk Calculators
# pooled cohort risk equations from 2013
test_that(
desc = "Goff supplement examples are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(3.0, 2.1, 6.1, 5.4)
)
}
)
test_that(
desc = 'some additional random examples work',
code = {
expect_equal(
round(
100 * predict_10yr_ascvd_risk(
age_years = 54,
sex = 'male',
race = 'white',
smoke_current = 'yes',
chol_total_mgdl = 170,
chol_hdl_mgdl = 50,
bp_sys_mmhg = 157,
bp_meds = 'no',
diabetes = 'yes'
),
digits = 1
),
20.8
)
}
)
test_that(
desc = 'NA values propagate',
code = {
expect_equal(
suppressWarnings(
predict_10yr_ascvd_risk(
age_years = 54,
sex = 'male',
race = 'white',
smoke_current = 'yes',
chol_total_mgdl = NA_real_,
chol_hdl_mgdl = 50,
bp_sys_mmhg = 157,
bp_meds = 'no',
diabetes = 'yes'
)
),
NA_real_
)
}
)
test_that(
desc = "examples run when different category levels are used",
code = {
sex = c('woman', 'woman', 'men', 'men')
race = c('AA', 'CA', 'AA', 'CA')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('No', times = 4)
# nonsmoker
smoke_current = rep('No', times = 4)
# without diabetes
diabetes = rep('No', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = 'CA'),
sex_levels = list(female = 'woman', male = 'men'),
smoke_current_levels = list(no = 'No', yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(3.0, 2.1, 6.1, 5.4)
)
}
)
test_that(
desc = "examples run in a data frame with different category levels",
code = {
some_data <- data.frame(
gender = c('woman', 'woman', 'man', 'male'),
race_3cats = c('AA', 'white', 'AA', 'other'),
# 55 years of age
age_years = rep(55, times = 4),
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4),
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4),
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4),
bp_meds = rep('No', times = 4),
# nonsmoker
smoke_current = c("no", "former", "no", "never"),
# without diabetes
diabetes = rep('No', times = 4),
stringsAsFactors = FALSE
)
pcr_probs <- with(
some_data,
predict_10yr_ascvd_risk(
sex = gender,
sex_levels = list(female = 'woman', male = c('man', 'male')),
race = race_3cats,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = c('white', 'other')),
smoke_current_levels = list(no = c('no', 'former', 'never'), yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
)
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(3.0, 2.1, 6.1, 5.4)
)
}
)
test_that(
desc = "examples run in a data frame with different category levels and NAs",
code = {
some_data <- data.frame(
gender = c('woman', NA_character_, 'man', 'male'),
race_3cats = c('AA', 'white', 'AA', 'other'),
# 55 years of age
age_years = rep(55, times = 4),
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4),
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4),
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4),
bp_meds = rep('No', times = 4),
# nonsmoker
smoke_current = c("no", "former", "no", "never"),
# without diabetes
diabetes = rep('No', times = 4),
stringsAsFactors = FALSE
)
expect_warning(
with(
some_data,
predict_10yr_ascvd_risk(
sex = gender,
sex_levels = list(female = 'woman', male = c('man', 'male')),
race = race_3cats,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = c('white', 'other')),
smoke_current_levels = list(no = c('no', 'former', 'never'), yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
)
),
'account for 1 missing values in the output'
)
pcr_probs <- suppressWarnings(
with(
some_data,
predict_10yr_ascvd_risk(
sex = gender,
sex_levels = list(female = 'woman', male = c('man', 'male')),
race = race_3cats,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = c('white', 'other')),
smoke_current_levels = list(no = c('no', 'former', 'never'), yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
)
)
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(3.0, NA, 6.1, 5.4)
)
}
)
test_that(
desc = "examples run in data with different category levels and factors",
code = {
some_data <- data.frame(
gender = c('woman', 'woman', 'man', 'male'),
race_3cats = c('AA', 'white', 'AA', 'other'),
# 55 years of age
age_years = rep(55, times = 4),
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4),
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4),
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4),
bp_meds = rep('No', times = 4),
# nonsmoker
smoke_current = c("no", "former", "no", "never"),
# without diabetes
diabetes = rep('No', times = 4),
stringsAsFactors = TRUE
)
pcr_probs <- with(
some_data,
predict_10yr_ascvd_risk(
sex = gender,
sex_levels = list(female = 'woman', male = c('man', 'male')),
race = race_3cats,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = c('white', 'other')),
smoke_current_levels = list(no = c('no', 'former', 'never'), yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
)
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(3.0, 2.1, 6.1, 5.4)
)
}
)
test_that(
desc = "error messages for incorrect levels are informative",
code = {
sex = c('woman', 'woman', 'men', 'man')
race = c('AA', 'CA', 'AA', 'CA')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('No', times = 4)
# nonsmoker
smoke_current = rep('No', times = 4)
# without diabetes
diabetes = rep('No', times = 4)
expect_error(
predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = 'CA'),
sex_levels = list(female = 'woman', male = 'men'),
smoke_current_levels = list(no = 'No', yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
),
regexp = 'instead has values <woman, men and man>'
)
age_years[1] <- 39
expect_error(
predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = 'CA'),
sex_levels = list(female = 'woman', male = c('men', 'man')),
smoke_current_levels = list(no = 'No', yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
),
regexp = 'min\\(age_years\\) is 39 but should be >= 40'
)
}
)
# pooled cohort risk equations from 2018
test_that(
desc = "Yadlowsky supplement examples are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
equation_version = 'Yadlowsky_2018'
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(1.8, 1.2, 3.5, 3.3)
)
}
)
test_that(
desc = "Goff supplement examples (with bp meds = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# treated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('yes', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(4.6, 2.8, 9.9, 6.3)
)
}
)
test_that(
desc = "Yadlowsky supplement examples (with bp meds = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('yes', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
equation_version = 'Yadlowsky_2018'
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(2.7, 1.6, 7.1, 4.7)
)
}
)
test_that(
desc = "Goff supplement examples (with smoker = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# smoker
smoke_current = rep('yes', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(5.9, 5.0, 10.3, 10.0)
)
}
)
test_that(
desc = "Yadlowsky supplement examples (with smoker = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('yes', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
equation_version = 'Yadlowsky_2018'
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(4.3, 3.2, 6.7, 7.8)
)
}
)
test_that(
desc = "Goff supplement examples (with diabetes = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# with diabetes
diabetes = rep('yes', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(7.0, 3.9, 11.2, 10.1)
)
}
)
test_that(
desc = "Yadlowsky supplement examples (with diabetes = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('yes', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
equation_version = 'Yadlowsky_2018'
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(4.9, 3.0, 7.3, 7.4)
)
}
)
test_that(
desc = "Replicate examples from Khan supplemental table S12",
code = {
prevent_base_10 <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(45, 45),
bp_sys_mmhg = c(160, 160),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
prevent_type = 'base'
)
expect_equal( round(prevent_base_10, 4), c(0.092, 0.1019) )
prevent_acr_10 <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(45, 45),
bp_sys_mmhg = c(160, 160),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
acr = c(40, 40),
prevent_type = 'acr'
)
expect_equal( round(prevent_acr_10, 4), c(.0994, .1097) )
prevent_hba1c_10 <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(45, 45),
bp_sys_mmhg = c(160, 160),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
hba1c = c(7.5, 7.5),
prevent_type = 'hba1c'
)
expect_equal( round(prevent_hba1c_10, 4), c(.0835, .0937) )
prevent_sdi_10 <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(45, 45),
bp_sys_mmhg = c(160, 160),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
sdi = c(8, 8),
prevent_type = 'sdi'
)
expect_equal( round(prevent_sdi_10, 4), c(.1003, .1137) )
prevent_full_10 <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(45, 45),
bp_sys_mmhg = c(160, 160),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
acr = c(40, 40),
hba1c = c(7.5, 7.5),
sdi = c(8, 8),
prevent_type = 'full'
)
expect_equal( round(prevent_full_10, 4), c(.0964, .1100))
prevent_full_missing_sdi <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(56, 56),
chol_total_mgdl = c(231, 231),
chol_hdl_mgdl = c(46, 46),
bp_sys_mmhg = c(150, 150),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
acr = c(900, 900),
hba1c = c(3, 3),
sdi = c(NA, NA),
prevent_type = 'full'
)
expect_equal( round(prevent_full_missing_sdi, 3), c(.077, .116))
prevent_full_missing_sdi <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(46, 46),
bp_sys_mmhg = c(169, 169),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
diabetes = c("no", "no"),
smoke_current = c("no", "no"),
bp_meds = c("no", "no"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
acr = c(40, 40),
hba1c = c(3, 3),
sdi = c(NA, NA),
prevent_type = 'full'
)
expect_equal( round(prevent_full_missing_sdi, 3), c(.026, .041))
}
)
test_that(
desc = "boundary overrides work",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(85, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
override_boundary_errors = TRUE
)
expect_is(pcr_probs, 'numeric')
}
)
bcjaeger/PooledCohort documentation built on March 10, 2024, 2:12 a.m.
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# pooled cohort risk equations from 2013
test_that(
desc = "Goff supplement examples are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(3.0, 2.1, 6.1, 5.4)
)
}
)
test_that(
desc = 'some additional random examples work',
code = {
expect_equal(
round(
100 * predict_10yr_ascvd_risk(
age_years = 54,
sex = 'male',
race = 'white',
smoke_current = 'yes',
chol_total_mgdl = 170,
chol_hdl_mgdl = 50,
bp_sys_mmhg = 157,
bp_meds = 'no',
diabetes = 'yes'
),
digits = 1
),
20.8
)
}
)
test_that(
desc = 'NA values propagate',
code = {
expect_equal(
suppressWarnings(
predict_10yr_ascvd_risk(
age_years = 54,
sex = 'male',
race = 'white',
smoke_current = 'yes',
chol_total_mgdl = NA_real_,
chol_hdl_mgdl = 50,
bp_sys_mmhg = 157,
bp_meds = 'no',
diabetes = 'yes'
)
),
NA_real_
)
}
)
test_that(
desc = "examples run when different category levels are used",
code = {
sex = c('woman', 'woman', 'men', 'men')
race = c('AA', 'CA', 'AA', 'CA')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('No', times = 4)
# nonsmoker
smoke_current = rep('No', times = 4)
# without diabetes
diabetes = rep('No', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = 'CA'),
sex_levels = list(female = 'woman', male = 'men'),
smoke_current_levels = list(no = 'No', yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(3.0, 2.1, 6.1, 5.4)
)
}
)
test_that(
desc = "examples run in a data frame with different category levels",
code = {
some_data <- data.frame(
gender = c('woman', 'woman', 'man', 'male'),
race_3cats = c('AA', 'white', 'AA', 'other'),
# 55 years of age
age_years = rep(55, times = 4),
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4),
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4),
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4),
bp_meds = rep('No', times = 4),
# nonsmoker
smoke_current = c("no", "former", "no", "never"),
# without diabetes
diabetes = rep('No', times = 4),
stringsAsFactors = FALSE
)
pcr_probs <- with(
some_data,
predict_10yr_ascvd_risk(
sex = gender,
sex_levels = list(female = 'woman', male = c('man', 'male')),
race = race_3cats,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = c('white', 'other')),
smoke_current_levels = list(no = c('no', 'former', 'never'), yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
)
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(3.0, 2.1, 6.1, 5.4)
)
}
)
test_that(
desc = "examples run in a data frame with different category levels and NAs",
code = {
some_data <- data.frame(
gender = c('woman', NA_character_, 'man', 'male'),
race_3cats = c('AA', 'white', 'AA', 'other'),
# 55 years of age
age_years = rep(55, times = 4),
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4),
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4),
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4),
bp_meds = rep('No', times = 4),
# nonsmoker
smoke_current = c("no", "former", "no", "never"),
# without diabetes
diabetes = rep('No', times = 4),
stringsAsFactors = FALSE
)
expect_warning(
with(
some_data,
predict_10yr_ascvd_risk(
sex = gender,
sex_levels = list(female = 'woman', male = c('man', 'male')),
race = race_3cats,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = c('white', 'other')),
smoke_current_levels = list(no = c('no', 'former', 'never'), yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
)
),
'account for 1 missing values in the output'
)
pcr_probs <- suppressWarnings(
with(
some_data,
predict_10yr_ascvd_risk(
sex = gender,
sex_levels = list(female = 'woman', male = c('man', 'male')),
race = race_3cats,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = c('white', 'other')),
smoke_current_levels = list(no = c('no', 'former', 'never'), yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
)
)
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(3.0, NA, 6.1, 5.4)
)
}
)
test_that(
desc = "examples run in data with different category levels and factors",
code = {
some_data <- data.frame(
gender = c('woman', 'woman', 'man', 'male'),
race_3cats = c('AA', 'white', 'AA', 'other'),
# 55 years of age
age_years = rep(55, times = 4),
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4),
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4),
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4),
bp_meds = rep('No', times = 4),
# nonsmoker
smoke_current = c("no", "former", "no", "never"),
# without diabetes
diabetes = rep('No', times = 4),
stringsAsFactors = TRUE
)
pcr_probs <- with(
some_data,
predict_10yr_ascvd_risk(
sex = gender,
sex_levels = list(female = 'woman', male = c('man', 'male')),
race = race_3cats,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = c('white', 'other')),
smoke_current_levels = list(no = c('no', 'former', 'never'), yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
)
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(3.0, 2.1, 6.1, 5.4)
)
}
)
test_that(
desc = "error messages for incorrect levels are informative",
code = {
sex = c('woman', 'woman', 'men', 'man')
race = c('AA', 'CA', 'AA', 'CA')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('No', times = 4)
# nonsmoker
smoke_current = rep('No', times = 4)
# without diabetes
diabetes = rep('No', times = 4)
expect_error(
predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = 'CA'),
sex_levels = list(female = 'woman', male = 'men'),
smoke_current_levels = list(no = 'No', yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
),
regexp = 'instead has values <woman, men and man>'
)
age_years[1] <- 39
expect_error(
predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
race_levels = list(black = 'AA', white = 'CA'),
sex_levels = list(female = 'woman', male = c('men', 'man')),
smoke_current_levels = list(no = 'No', yes = 'Yes'),
bp_meds_levels = list(no = 'No', yes = 'Yes'),
diabetes_levels = list(no = 'No', yes = 'Yes')
),
regexp = 'min\\(age_years\\) is 39 but should be >= 40'
)
}
)
# pooled cohort risk equations from 2018
test_that(
desc = "Yadlowsky supplement examples are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
equation_version = 'Yadlowsky_2018'
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(1.8, 1.2, 3.5, 3.3)
)
}
)
test_that(
desc = "Goff supplement examples (with bp meds = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# treated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('yes', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(4.6, 2.8, 9.9, 6.3)
)
}
)
test_that(
desc = "Yadlowsky supplement examples (with bp meds = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('yes', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
equation_version = 'Yadlowsky_2018'
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(2.7, 1.6, 7.1, 4.7)
)
}
)
test_that(
desc = "Goff supplement examples (with smoker = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# smoker
smoke_current = rep('yes', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(5.9, 5.0, 10.3, 10.0)
)
}
)
test_that(
desc = "Yadlowsky supplement examples (with smoker = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('yes', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
equation_version = 'Yadlowsky_2018'
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(4.3, 3.2, 6.7, 7.8)
)
}
)
test_that(
desc = "Goff supplement examples (with diabetes = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# with diabetes
diabetes = rep('yes', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(7.0, 3.9, 11.2, 10.1)
)
}
)
test_that(
desc = "Yadlowsky supplement examples (with diabetes = yes) are correct",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(55, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('yes', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
equation_version = 'Yadlowsky_2018'
)
# to match online format
pcr_perc_rounded <- round(pcr_probs * 100, 1)
expect_equal(
pcr_perc_rounded,
c(4.9, 3.0, 7.3, 7.4)
)
}
)
test_that(
desc = "Replicate examples from Khan supplemental table S12",
code = {
prevent_base_10 <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(45, 45),
bp_sys_mmhg = c(160, 160),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
prevent_type = 'base'
)
expect_equal( round(prevent_base_10, 4), c(0.092, 0.1019) )
prevent_acr_10 <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(45, 45),
bp_sys_mmhg = c(160, 160),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
acr = c(40, 40),
prevent_type = 'acr'
)
expect_equal( round(prevent_acr_10, 4), c(.0994, .1097) )
prevent_hba1c_10 <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(45, 45),
bp_sys_mmhg = c(160, 160),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
hba1c = c(7.5, 7.5),
prevent_type = 'hba1c'
)
expect_equal( round(prevent_hba1c_10, 4), c(.0835, .0937) )
prevent_sdi_10 <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(45, 45),
bp_sys_mmhg = c(160, 160),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
sdi = c(8, 8),
prevent_type = 'sdi'
)
expect_equal( round(prevent_sdi_10, 4), c(.1003, .1137) )
prevent_full_10 <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(45, 45),
bp_sys_mmhg = c(160, 160),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
acr = c(40, 40),
hba1c = c(7.5, 7.5),
sdi = c(8, 8),
prevent_type = 'full'
)
expect_equal( round(prevent_full_10, 4), c(.0964, .1100))
prevent_full_missing_sdi <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(56, 56),
chol_total_mgdl = c(231, 231),
chol_hdl_mgdl = c(46, 46),
bp_sys_mmhg = c(150, 150),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
diabetes = c("yes", "yes"),
smoke_current = c("no", "no"),
bp_meds = c("yes", "yes"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
acr = c(900, 900),
hba1c = c(3, 3),
sdi = c(NA, NA),
prevent_type = 'full'
)
expect_equal( round(prevent_full_missing_sdi, 3), c(.077, .116))
prevent_full_missing_sdi <- predict_10yr_ascvd_risk(
sex = c("female", "male"),
age_years = c(50, 50),
chol_total_mgdl = c(200, 200),
chol_hdl_mgdl = c(46, 46),
bp_sys_mmhg = c(169, 169),
bmi = c(35, 35),
egfr_mlminm2 = c(90, 90),
diabetes = c("no", "no"),
smoke_current = c("no", "no"),
bp_meds = c("no", "no"),
statin_meds = c("no", "no"),
equation_version = "Khan_2023",
acr = c(40, 40),
hba1c = c(3, 3),
sdi = c(NA, NA),
prevent_type = 'full'
)
expect_equal( round(prevent_full_missing_sdi, 3), c(.026, .041))
}
)
test_that(
desc = "boundary overrides work",
code = {
sex = c('female', 'female', 'male', 'male')
race = c('black', 'white', 'black', 'white')
# 55 years of age
age_years = rep(85, times = 4)
# total cholesterol 213 mg/dL
chol_total_mgdl = rep(213, times = 4)
# HDL–C 50 mg/dL
chol_hdl_mgdl = rep(50, times = 4)
# untreated systolic BP 120 mm Hg
bp_sys_mmhg = rep(120, times = 4)
bp_meds = rep('no', times = 4)
# nonsmoker
smoke_current = rep('no', times = 4)
# without diabetes
diabetes = rep('no', times = 4)
pcr_probs <- predict_10yr_ascvd_risk(
sex = sex,
race = race,
age_years = age_years,
chol_total_mgdl = chol_total_mgdl,
chol_hdl_mgdl = chol_hdl_mgdl,
bp_sys_mmhg = bp_sys_mmhg,
bp_meds = bp_meds,
smoke_current = smoke_current,
diabetes = diabetes,
override_boundary_errors = TRUE
)
expect_is(pcr_probs, 'numeric')
}
)
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