R/CVDAnderson.R
In Aidakazemi/CVDAnderson: Cardiovascular Disease Risk Prediction Models based on <doi: 10.1016/0002-8703(91)90861-b>
#' Predicting Cardiovascular disease risk base on systolic or diastolic blood pressure
#'
#' @param gender A binary variable taking 1 if a patient is female and 0 if male
#' @param age A number for age
#' @param Tchol A number for total serum cholesterol in mg/dL measured by the Abell-Kendall method
#' @param HDLchol A number for high-density lipoprotein (HDL) Cholesterol in mg/dL
#' @param SBP A number fo systolic blood pressure in mm Hg
#' @param DBP A number for diastolic blood pressure in mm Hg
#' @param diabetes A binary variable taking 1 if the patient was under treatment with insulin or oral hypoglycemic agents, if casual blood glucose determinations exceeded 140 mg/dL
#' @param smoker A binary variable taking 1 for person who smoked during the past 12 month and 0therwise
#' @param ECG_LVH A binary variable taking 1 for patients diagnosed with ECG-left ventricular hypertrophy and 0 otherwise (If there is no information, it is 0)
#' @param t A number for time until CVD from the beginning of follow-up
#' @value returns risk of cardiovascular events based on either systolic or diastolic blood pressure
#'
#' @example
#' predictcvd (gender = 1, age = 33, Tchol = 230, HDLchol = 48, SBP = 135, DBP = 88, diabetes = 1, smoker = 1, ECG_LVH = 0, t = 10)
#' @source \url{https://www.ahajournals.org/doi/pdf/10.1161/01.CIR.83.1.356} and \url{https://www.sciencedirect.com/science/article/pii/000287039190861B}
#' @export
predictcvd <- function (gender, age, Tchol, HDLchol, SBP, DBP, diabetes, smoker, ECG_LVH, t) {
#============================================================= SBP prediction =====================================================================================
m_CHD_s <- 15.5305 + (28.4441 * gender) - (1.4792 * log (age)) - (0 * (log (age)) ^ 2) - (14.4588 * (log (age)) * gender) + (1.8515 * (log (age)) ^ 2 * gender) - (0.9119 * log (SBP)) - (0.2767 * smoker) - (0.7181 * log (Tchol / HDLchol)) - (0.1759 * diabetes) - (0.1999 * diabetes * gender) - (0.5865 * ECG_LVH) - (0 * ECG_LVH * (1 - gender))
m_MI_s <- 11.4712 + 10.5109 * gender - 0.7965 * log (age) - 0 * log (age) ^ 2 - 5.4216 * log (age) * gender + 0.7101 * log (age) ^ 2 * gender - 0.6623 * log (SBP) - 0.2675 * smoker - 0.4277 * log (Tchol / HDLchol) - 0.1534 * diabetes - 0.1165 * diabetes * gender - 0 * ECG_LVH - 0.1588 * ECG_LVH * (1 - gender)
m_CHDdeath_s <- 11.2889 + 0.2332 * gender - 0.9440 * log (age) - 0 * log (age ^ 2) - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 0.5880 * log (SBP) - 0.1367 * smoker - 0.3448 * log (Tchol / HDLchol) - 0.0474 * diabetes - 0.2233 * diabetes * gender - 0.1237 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_stroke_s <- 26.5116 + 0.2019 * gender - 2.3741 * log (age) - 0 * log (age) ^ 2 - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 2.4643 * log (SBP) - 0.3914 * smoker - 0.0229 * log (Tchol / HDLchol) - 0.3087 * diabetes - 0.2627 * diabetes * gender - 0.2355 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_CVD_s <- 18.8144 - 1.2146 * gender - 1.8443 * log (age) - 0 * log (age) ^ 2 + 0.3668 * log (age) * gender + 0 * log (age) ^ 2 * gender - 1.4032 * log (SBP) - 0.3899 * smoker - 0.5390 * log (Tchol / HDLchol) - 0.3036 * diabetes - 0.1697 * diabetes * gender - 0.3362 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_CVDdeath_s <- -5.0385 + 0.2243 * gender + 8.2370 * log (age) - 1.2109 * log (age) ^ 2 - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 0.8383 * log (SBP) - 0.1618 * smoker - 0.3493 * log (Tchol / HDLchol) - 0.0833 * diabetes - 0.2067 * diabetes * gender - 0.2946 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
log_sigma_CHD_s <- 0.9145 - 0.2784 * m_CHD_s
log_sigma_MI_s <- 3.4064 - 0.8584 * m_MI_s
log_sigma_CHDdeath_s <- 2.9851 - 0.9142 * m_CHDdeath_s
log_sigma_stroke_s <- -0.4312 - 0 * m_stroke_s
log_sigma_CVD_s <- 0.6536 - 0.2402 * m_CVD_s
log_sigma_CVDdeath_s <- 0.8207 - 0.4346 * m_CVDdeath_s
sigma_CHD_s <- exp (log_sigma_CHD_s)
sigma_MI_s <- exp (log_sigma_MI_s)
sigma_CHDdeath_s <- exp (log_sigma_CHDdeath_s)
sigma_stroke_s <- exp (log_sigma_stroke_s)
sigma_CVD_s <- exp (log_sigma_CVD_s)
sigma_CVDdeath_s <- exp (log_sigma_CVDdeath_s)
u_CHD_s <- (log(t) - m_CHD_s) / sigma_CHD_s
u_MI_s <- (log(t) - m_MI_s) / sigma_MI_s
u_CHDdeath_s <- (log(t) - m_CHDdeath_s) / sigma_CHDdeath_s
u_stroke_s <- (log(t) - m_stroke_s) / sigma_stroke_s
u_CVD_s <- (log(t) - m_CVD_s) / sigma_CVD_s
u_CVDdeath_s <- (log(t) - m_CVDdeath_s) / sigma_CVDdeath_s
p_CHD_s <- round (1 - exp(-exp(u_CHD_s)), digits =2)
p_MI_s <- round (1 - exp(-exp(u_MI_s)), digits =2)
p_CHDdeath_s <- round (1 - exp(-exp(u_CHDdeath_s)), digits =2)
p_stroke_s <- round (1 - exp(-exp(u_stroke_s)), digits =2)
p_CVD_s <- round (1 - exp(-exp(u_CVD_s)), digits =2)
p_CVDdeath_s <- round (1 - exp(-exp(u_CVDdeath_s)), digits =2)
#============================================================= DBP prediction =====================================================================================
m_CHD_d <- 15.5222 + 32.4811 * gender - 1.6347 * log (age) - 0 * log (age) ^ 2 - 16.4933 * log (age) * gender + 2.1059 * log (age) ^ 2 * gender - 0.8670 * log (DBP) - 0.2789 * smoker - 0.7142 * log (Tchol / HDLchol) - 0.2082 * diabetes - 0.1973 * diabetes * gender - 0.7195 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_MI_d <- 11.0436 + 5.1559 * gender - 0.9302 * log (age) - 0 * log (age) ^ 2 - 2.6310 * log (age) * gender + 0.3472 * log (age) ^ 2 * gender - 0.5132 * log (DBP) - 0.2721 * smoker - 0.4228 * log (Tchol / HDLchol) - 0.1764 * diabetes - 0.1184 * diabetes * gender - 0 * ECG_LVH - 0.1702 * ECG_LVH * (1 - gender)
m_CHDdeath_d <- 12.0963 + 0.2619 * gender - 1.3025 * log (age) - 0 * log (age) ^ 2 - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 0.4762 * log (DBP) - 0.1553 * smoker - 0.4056 * log (Tchol / HDLchol) - 0.0860 * diabetes - 0.2539 * diabetes * gender - 0.1591 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_stroke_d <- 25.1067 + 0.1558 * gender - 3.0997 * log (age) - 0 * log (age) ^ 2 - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 1.7556 * log (DBP) - 0.3975 * smoker - 0.0297 * log (Tchol / HDLchol) - 0.4047 * diabetes - 0.2506 * diabetes * gender - 0.2801 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_CVD_d <- 17.5392 - 0.8019 * gender - 2.1231 * log (age) - 0 * log (age) ^ 2 + 0.2584 * log (age) * gender + 0 * log (age) ^ 2 * gender - 1.0117 * log (DBP) - 0.3900 * smoker - 0.5365 * log (Tchol / HDLchol) - 0.3575 * diabetes - 0.1661 * diabetes * gender - 0.3847 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_CVDdeath_d <- -9.0211 + 0.2102 * gender + 9.5223 * log (age) - 1.3999 * log (age) ^ 2 - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 0.5073 * log (DBP) - 0.1548 * smoker - 0.3423 * log (Tchol / HDLchol) - 0.1178 * diabetes - 0.1982 * diabetes * gender - 0.3181 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
log_sigma_CHD_d <- 0.9341 - 0.2825 * m_CHD_d
log_sigma_MI_d <- 3.4587 - 0.8647 * m_MI_d
log_sigma_CHDdeath_d <- 2.1249 - 0.6860 * m_CHDdeath_d
log_sigma_stroke_d <- -0.4212 - 0 * m_stroke_d
log_sigma_CVD_d <- 0.6761 - 0.2421 * m_CVD_d
log_sigma_CVDdeath_d <- 0.9076 - 0.4528 * m_CVDdeath_d
sigma_CHD_d <- exp (log_sigma_CHD_d)
sigma_MI_d <- exp (log_sigma_MI_d)
sigma_CHDdeath_d <- exp (log_sigma_CHDdeath_d)
sigma_stroke_d <- exp (log_sigma_stroke_d)
sigma_CVD_d <- exp (log_sigma_CVD_d)
sigma_CVDdeath_d <- exp (log_sigma_CVDdeath_d)
u_CHD_d <- (log(t) - m_CHD_d) / sigma_CHD_d
u_MI_d <- (log(t) - m_MI_d) / sigma_MI_d
u_CHDdeath_d <- (log(t) - m_CHDdeath_d) / sigma_CHDdeath_d
u_stroke_d <- (log(t) - m_stroke_d) / sigma_stroke_d
u_CVD_d <- (log(t) - m_CVD_d) / sigma_CVD_d
u_CVDdeath_d <- (log(t) - m_CVDdeath_d) / sigma_CVDdeath_d
p_CHD_d <- round (1 - exp(-exp(u_CHD_d)), digits = 2)
p_MI_d <- round (1 - exp(-exp(u_MI_d)), digits =2)
p_CHDdeath_d <- round (1 - exp(-exp(u_CHDdeath_d)), digits =2)
p_stroke_d <- round (1 - exp(-exp(u_stroke_d)), digits =2)
p_CVD_d <- round (1 - exp(-exp(u_CVD_d)), digits =2)
p_CVDdeath_d <- round(1 - exp(-exp(u_CVDdeath_d)), digits =2)
results <- list()
if (age > 29 & age < 75) {
results <- list("CHD_risk_systolicBase" = p_CHD_s,
"MI_risk_systolicBase" = p_CHD_s,
"CHDdeath_risk_systolicBase" = p_CHDdeath_s,
"stroke_risk_systolicBase" = p_stroke_s,
"CVD_risk_systolicBase" = p_CVD_s,
"CVDdeath_risk_systolicBase" = p_CVDdeath_s,
"CHD_risk_diastolicBase" = p_CHD_d,
"MI_risk_diastolicBase" = p_CHD_d,
"CHDdeath_risk_diastolicBase" = p_CHDdeath_d,
"stroke_risk_diastolicBase" = p_stroke_d,
"CVD_risk_diastolicBase" = p_CVD_d,
"CVDdeath_risk_diastolicBase" = p_CVDdeath_d)
return(as.list(results))
} else {
stop("Age out of range. Age should be between 29 and 75")
}
}
Aidakazemi/CVDAnderson documentation built on Aug. 21, 2021, 9:45 a.m.
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#' Predicting Cardiovascular disease risk base on systolic or diastolic blood pressure
#'
#' @param gender A binary variable taking 1 if a patient is female and 0 if male
#' @param age A number for age
#' @param Tchol A number for total serum cholesterol in mg/dL measured by the Abell-Kendall method
#' @param HDLchol A number for high-density lipoprotein (HDL) Cholesterol in mg/dL
#' @param SBP A number fo systolic blood pressure in mm Hg
#' @param DBP A number for diastolic blood pressure in mm Hg
#' @param diabetes A binary variable taking 1 if the patient was under treatment with insulin or oral hypoglycemic agents, if casual blood glucose determinations exceeded 140 mg/dL
#' @param smoker A binary variable taking 1 for person who smoked during the past 12 month and 0therwise
#' @param ECG_LVH A binary variable taking 1 for patients diagnosed with ECG-left ventricular hypertrophy and 0 otherwise (If there is no information, it is 0)
#' @param t A number for time until CVD from the beginning of follow-up
#' @value returns risk of cardiovascular events based on either systolic or diastolic blood pressure
#'
#' @example
#' predictcvd (gender = 1, age = 33, Tchol = 230, HDLchol = 48, SBP = 135, DBP = 88, diabetes = 1, smoker = 1, ECG_LVH = 0, t = 10)
#' @source \url{https://www.ahajournals.org/doi/pdf/10.1161/01.CIR.83.1.356} and \url{https://www.sciencedirect.com/science/article/pii/000287039190861B}
#' @export
predictcvd <- function (gender, age, Tchol, HDLchol, SBP, DBP, diabetes, smoker, ECG_LVH, t) {
#============================================================= SBP prediction =====================================================================================
m_CHD_s <- 15.5305 + (28.4441 * gender) - (1.4792 * log (age)) - (0 * (log (age)) ^ 2) - (14.4588 * (log (age)) * gender) + (1.8515 * (log (age)) ^ 2 * gender) - (0.9119 * log (SBP)) - (0.2767 * smoker) - (0.7181 * log (Tchol / HDLchol)) - (0.1759 * diabetes) - (0.1999 * diabetes * gender) - (0.5865 * ECG_LVH) - (0 * ECG_LVH * (1 - gender))
m_MI_s <- 11.4712 + 10.5109 * gender - 0.7965 * log (age) - 0 * log (age) ^ 2 - 5.4216 * log (age) * gender + 0.7101 * log (age) ^ 2 * gender - 0.6623 * log (SBP) - 0.2675 * smoker - 0.4277 * log (Tchol / HDLchol) - 0.1534 * diabetes - 0.1165 * diabetes * gender - 0 * ECG_LVH - 0.1588 * ECG_LVH * (1 - gender)
m_CHDdeath_s <- 11.2889 + 0.2332 * gender - 0.9440 * log (age) - 0 * log (age ^ 2) - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 0.5880 * log (SBP) - 0.1367 * smoker - 0.3448 * log (Tchol / HDLchol) - 0.0474 * diabetes - 0.2233 * diabetes * gender - 0.1237 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_stroke_s <- 26.5116 + 0.2019 * gender - 2.3741 * log (age) - 0 * log (age) ^ 2 - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 2.4643 * log (SBP) - 0.3914 * smoker - 0.0229 * log (Tchol / HDLchol) - 0.3087 * diabetes - 0.2627 * diabetes * gender - 0.2355 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_CVD_s <- 18.8144 - 1.2146 * gender - 1.8443 * log (age) - 0 * log (age) ^ 2 + 0.3668 * log (age) * gender + 0 * log (age) ^ 2 * gender - 1.4032 * log (SBP) - 0.3899 * smoker - 0.5390 * log (Tchol / HDLchol) - 0.3036 * diabetes - 0.1697 * diabetes * gender - 0.3362 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_CVDdeath_s <- -5.0385 + 0.2243 * gender + 8.2370 * log (age) - 1.2109 * log (age) ^ 2 - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 0.8383 * log (SBP) - 0.1618 * smoker - 0.3493 * log (Tchol / HDLchol) - 0.0833 * diabetes - 0.2067 * diabetes * gender - 0.2946 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
log_sigma_CHD_s <- 0.9145 - 0.2784 * m_CHD_s
log_sigma_MI_s <- 3.4064 - 0.8584 * m_MI_s
log_sigma_CHDdeath_s <- 2.9851 - 0.9142 * m_CHDdeath_s
log_sigma_stroke_s <- -0.4312 - 0 * m_stroke_s
log_sigma_CVD_s <- 0.6536 - 0.2402 * m_CVD_s
log_sigma_CVDdeath_s <- 0.8207 - 0.4346 * m_CVDdeath_s
sigma_CHD_s <- exp (log_sigma_CHD_s)
sigma_MI_s <- exp (log_sigma_MI_s)
sigma_CHDdeath_s <- exp (log_sigma_CHDdeath_s)
sigma_stroke_s <- exp (log_sigma_stroke_s)
sigma_CVD_s <- exp (log_sigma_CVD_s)
sigma_CVDdeath_s <- exp (log_sigma_CVDdeath_s)
u_CHD_s <- (log(t) - m_CHD_s) / sigma_CHD_s
u_MI_s <- (log(t) - m_MI_s) / sigma_MI_s
u_CHDdeath_s <- (log(t) - m_CHDdeath_s) / sigma_CHDdeath_s
u_stroke_s <- (log(t) - m_stroke_s) / sigma_stroke_s
u_CVD_s <- (log(t) - m_CVD_s) / sigma_CVD_s
u_CVDdeath_s <- (log(t) - m_CVDdeath_s) / sigma_CVDdeath_s
p_CHD_s <- round (1 - exp(-exp(u_CHD_s)), digits =2)
p_MI_s <- round (1 - exp(-exp(u_MI_s)), digits =2)
p_CHDdeath_s <- round (1 - exp(-exp(u_CHDdeath_s)), digits =2)
p_stroke_s <- round (1 - exp(-exp(u_stroke_s)), digits =2)
p_CVD_s <- round (1 - exp(-exp(u_CVD_s)), digits =2)
p_CVDdeath_s <- round (1 - exp(-exp(u_CVDdeath_s)), digits =2)
#============================================================= DBP prediction =====================================================================================
m_CHD_d <- 15.5222 + 32.4811 * gender - 1.6347 * log (age) - 0 * log (age) ^ 2 - 16.4933 * log (age) * gender + 2.1059 * log (age) ^ 2 * gender - 0.8670 * log (DBP) - 0.2789 * smoker - 0.7142 * log (Tchol / HDLchol) - 0.2082 * diabetes - 0.1973 * diabetes * gender - 0.7195 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_MI_d <- 11.0436 + 5.1559 * gender - 0.9302 * log (age) - 0 * log (age) ^ 2 - 2.6310 * log (age) * gender + 0.3472 * log (age) ^ 2 * gender - 0.5132 * log (DBP) - 0.2721 * smoker - 0.4228 * log (Tchol / HDLchol) - 0.1764 * diabetes - 0.1184 * diabetes * gender - 0 * ECG_LVH - 0.1702 * ECG_LVH * (1 - gender)
m_CHDdeath_d <- 12.0963 + 0.2619 * gender - 1.3025 * log (age) - 0 * log (age) ^ 2 - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 0.4762 * log (DBP) - 0.1553 * smoker - 0.4056 * log (Tchol / HDLchol) - 0.0860 * diabetes - 0.2539 * diabetes * gender - 0.1591 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_stroke_d <- 25.1067 + 0.1558 * gender - 3.0997 * log (age) - 0 * log (age) ^ 2 - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 1.7556 * log (DBP) - 0.3975 * smoker - 0.0297 * log (Tchol / HDLchol) - 0.4047 * diabetes - 0.2506 * diabetes * gender - 0.2801 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_CVD_d <- 17.5392 - 0.8019 * gender - 2.1231 * log (age) - 0 * log (age) ^ 2 + 0.2584 * log (age) * gender + 0 * log (age) ^ 2 * gender - 1.0117 * log (DBP) - 0.3900 * smoker - 0.5365 * log (Tchol / HDLchol) - 0.3575 * diabetes - 0.1661 * diabetes * gender - 0.3847 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
m_CVDdeath_d <- -9.0211 + 0.2102 * gender + 9.5223 * log (age) - 1.3999 * log (age) ^ 2 - 0 * log (age) * gender + 0 * log (age) ^ 2 * gender - 0.5073 * log (DBP) - 0.1548 * smoker - 0.3423 * log (Tchol / HDLchol) - 0.1178 * diabetes - 0.1982 * diabetes * gender - 0.3181 * ECG_LVH - 0 * ECG_LVH * (1 - gender)
log_sigma_CHD_d <- 0.9341 - 0.2825 * m_CHD_d
log_sigma_MI_d <- 3.4587 - 0.8647 * m_MI_d
log_sigma_CHDdeath_d <- 2.1249 - 0.6860 * m_CHDdeath_d
log_sigma_stroke_d <- -0.4212 - 0 * m_stroke_d
log_sigma_CVD_d <- 0.6761 - 0.2421 * m_CVD_d
log_sigma_CVDdeath_d <- 0.9076 - 0.4528 * m_CVDdeath_d
sigma_CHD_d <- exp (log_sigma_CHD_d)
sigma_MI_d <- exp (log_sigma_MI_d)
sigma_CHDdeath_d <- exp (log_sigma_CHDdeath_d)
sigma_stroke_d <- exp (log_sigma_stroke_d)
sigma_CVD_d <- exp (log_sigma_CVD_d)
sigma_CVDdeath_d <- exp (log_sigma_CVDdeath_d)
u_CHD_d <- (log(t) - m_CHD_d) / sigma_CHD_d
u_MI_d <- (log(t) - m_MI_d) / sigma_MI_d
u_CHDdeath_d <- (log(t) - m_CHDdeath_d) / sigma_CHDdeath_d
u_stroke_d <- (log(t) - m_stroke_d) / sigma_stroke_d
u_CVD_d <- (log(t) - m_CVD_d) / sigma_CVD_d
u_CVDdeath_d <- (log(t) - m_CVDdeath_d) / sigma_CVDdeath_d
p_CHD_d <- round (1 - exp(-exp(u_CHD_d)), digits = 2)
p_MI_d <- round (1 - exp(-exp(u_MI_d)), digits =2)
p_CHDdeath_d <- round (1 - exp(-exp(u_CHDdeath_d)), digits =2)
p_stroke_d <- round (1 - exp(-exp(u_stroke_d)), digits =2)
p_CVD_d <- round (1 - exp(-exp(u_CVD_d)), digits =2)
p_CVDdeath_d <- round(1 - exp(-exp(u_CVDdeath_d)), digits =2)
results <- list()
if (age > 29 & age < 75) {
results <- list("CHD_risk_systolicBase" = p_CHD_s,
"MI_risk_systolicBase" = p_CHD_s,
"CHDdeath_risk_systolicBase" = p_CHDdeath_s,
"stroke_risk_systolicBase" = p_stroke_s,
"CVD_risk_systolicBase" = p_CVD_s,
"CVDdeath_risk_systolicBase" = p_CVDdeath_s,
"CHD_risk_diastolicBase" = p_CHD_d,
"MI_risk_diastolicBase" = p_CHD_d,
"CHDdeath_risk_diastolicBase" = p_CHDdeath_d,
"stroke_risk_diastolicBase" = p_stroke_d,
"CVD_risk_diastolicBase" = p_CVD_d,
"CVDdeath_risk_diastolicBase" = p_CVDdeath_d)
return(as.list(results))
} else {
stop("Age out of range. Age should be between 29 and 75")
}
}
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