R/NoPriorCVDRisk_BMI.R
In billy-nz/PredictRiskScores: Functions for generating PREDICT CVD risk scores
Defines functions
NoPriorCVDRisk_BMI
Documented in
NoPriorCVDRisk_BMI
#' PREDICT CVD (2018.2) Risk Score for People Without Prior CVD
#'
#' \code{NoPriorCVDRisk_BMI} calculates the 5 year absolute risk of cardiovascular disease (CVD) for people without a history of atherosclerotic CVD.
#' In this version, BMI is used as a predictor. The outcome of interest is the 5-year risk of a non-fatal or fatal CVD event, including hospitalisation
#' for coronary heart disease, stroke or other cerebrovascular disease (including transient ischaemic attack), peripheral vascular disease and heart failure,
#' or cardiovascular death.
#'
#' @usage NoPriorCVDRisk_BMI(dat, sex, age, eth, nzdep, exsmoker, smoker, diabetes,
#' af, familyhx, sbp, tchdl, bmi, bpl, lld, athrombi, ...)
#'
#' @inheritParams NoPriorCVDRisk
#' @param bmi body mass index in kg/m^2
#'
#' @inherit NoPriorCVDRisk details
#'
#' @return
#' returns either a single 5-year CVD risk estimate, or a numeric vector of risk estimates if \code{dat} is provided.
#' Input values for each parameter must conform to the following convention:
#'
#' \item{sex}{label or encode as one of the following:
#' \itemize{
#' \item M, Male, 1
#' \item F, Female, 0
#' }}
#' \item{age}{numeric value for years of age between 20 and 110}
#' \item{eth}{label or encode as one of the following:
#' \itemize{
#' \item NZ European, European, NZEO, Euro, E, 1, 10, 11, or 12
#' \item Maori, NZMaori, NZ Maori, M, 2, or 21
#' \item Pacific, Pacific Islander, PI, P, 3, 30, 31, 32, 33, 34, 35, 36, or 37
#' \item Indian, Fijian Indian, South Asian, IN, I, or 43
#' \item Asian, Other Asian, SE Asian, East Asian, Chinese, ASN, A, 4, 40, 41, 42, or 44
#' \item note: Other Asian includes non-Indian South Asian
#' }}
#' \item{nzdep}{numeric value between 1 and 5}
#' \item{exsmoker}{label or encode as one of the following:
#' \itemize{
#' \item Y, Yes, Ex, Ex-smoker, Exsmoker, E, 1, T, TRUE
#' \item N, No, Non-smoker, Non, 0, F, FALSE
#' }}
#' \item{smoker}{label or encode as one of the following:
#' \itemize{
#' \item Y, Yes, Smoker, Current, S, 1, T, TRUE
#' \item N, No, Non-smoker, Non, 0, F, FALSE
#' }}
#' \item{diabetes,\cr af, hf}{label or encode as one of the following:
#' \itemize{
#' \item Y, Yes, 1, T, TRUE
#' \item N, No, 0, F, FALSE
#' }}
#' \item{bpl, lld,\cr athrombi}{label or encode as one of the following:
#' \itemize{
#' \item Y, Yes, 1, T, TRUE
#' \item N, No, 0, F, FALSE
#' }}
#' \item{sbp, tchdl}{numeric value of measured result. Note:
#' \itemize{
#' \item SBP and total:HDL values must be available
#' }}
#' \item{bmi}{numeric value of calculated BMI. If BMI is unknown, input as \code{NA}}
#' \item{...}{further arguments:
#' \itemize{
#' \item \code{dp} numeric value to set decimal place; default is 4
#' \item \code{allow.age} logical. Whether or not age range is extended outside of 30 - 74; default is TRUE. If set to FALSE, then \code{NA} is returned as risk estimate.
#' \item \code{allow.na} logical. Whether or not missing values for binary variables and smoking status are treated as 0; default is TRUE. If set to FALSE, then \code{NA} is returned as risk estimate.
#' }}
#'
#' @inheritSection NoPriorCVDRisk See Also
#'
#' @author
#' Billy Wu (R Developer) and Romana Pylypchuk (Principal Investigator)
#'
#' @references
#' New Zealand Ministry of Health: HISO 10071:2019 Cardiovascular Disease Risk Assessment Data Standard
#'
#' \href{https://www.health.govt.nz/publication/hiso-100712019-cardiovascular-disease-risk-assessment-data-standard}{HISO Document}
#'
#' @export
#' @examples
#' # As calculator (dataset not provided)
#' NoPriorCVDRisk_BMI(sex="F", age=65, eth="Indian", smoker=0, nzdep=5, diabetes=0,
#' af=0, familyhx=1, lld=1, athrombi=1, bpl=1, sbp=118, tchdl=3.3, bmi=32)
#'
#' NoPriorCVDRisk_BMI(sex=F, age=55, eth=IN, exsmoker=Y, smoker=0, nzdep=5, diabetes=T,
#' af=Y, familyhx=T, lld=1, athrombi=Y, bpl=T, sbp=120, tchdl=3.2, bmi=42)
#'
#' # As a vectoriser (dataset provided)
#' NoPriorCVDRisk_BMI(dat=DF, sex=sex, age=age, eth=ethnic_labels, smoker=smoking_status, nzdep=nzdep_quintiles,
#' diabetes=diab_status, af=af, familyhx=fam_hx, lld=lipidlowering, athrombi=antithrombics,
#' bpl=bplowering, sbp=systolic_bp, tchdl=tchdl_ratio, bmi=bmi)
#'
# --- Code ---
NoPriorCVDRisk_BMI <- function(dat, sex, age, eth, nzdep, exsmoker, smoker, diabetes, af, familyhx, sbp, tchdl, bmi, bpl, lld, athrombi,...){
# Params
demo.vars <- c("sex", "age", "eth", "nzdep")
smk.vars <- c("exsmoker", "smoker")
bin.vars <- c("diabetes", "af", "familyhx", "lld", "athrombi", "bpl")
num.vars <- c("sbp", "tchdl")
numNA.vars <- c("bmi")
# Calls
call <- gsub("()", "", match.call()[1])
is.table <- deparse(substitute(dat))!=""
input <- as.list(match.call()[-1])
if(length(list(...)) == 0){
dp <- 4
allow.age <- TRUE
allow.na <- TRUE
} else {
default <- setdiff(c("dp", "allow.age", "allow.na"), names(list(...)))
if(length(default) %in% 1:2){
lapply(default,
function(x){
if(x == "dp"){
val <- 4
} else if(x == "allow.na") {
val <- TRUE
} else {
val <- TRUE
}
assign(x, val, envir = parent.frame(2))
})
}
lapply(names(list(...)),
function(x)
assign(x, unlist(list(...)[x]),
envir = parent.frame(2)))
}
# ParamCheck
vars <- c(demo.vars, bin.vars, smk.vars, num.vars, numNA.vars)
ParamCheck(input, vars, call, is.table, allow.age, allow.na)
# Values
f.ind <- which(tolower(input$sex) %in% ok.female)
m.ind <- which(tolower(input$sex) %in% ok.male)
demo.vals <- list(age = input$age,
maori = +(tolower(input$eth) %in% ok.maori),
pacific = +(tolower(input$eth) %in% ok.pi),
indian = +(tolower(input$eth) %in% ok.indian),
asian = +(tolower(input$eth) %in% ok.asian),
exsmoker = +(tolower(input$exsmoker) %in% ok.exsmkr),
smoker = +(tolower(input$smoker) %in% ok.smoker),
nzdep = input$nzdep)
bin.vals <- sapply(bin.vars,
function(x){
+(tolower(input[[x]]) %in% ok.true)
},
USE.NAMES = TRUE,
simplify = FALSE)
num.vals <- sapply(num.vars,
function(x){
as.numeric(input[[x]])
},
USE.NAMES = TRUE,
simplify = FALSE)
bmi <- list(bmilt185 = +(input$bmi < 18.5 & !is.na(input$bmi)),
bmi25_30 = +(input$bmi %in% 25:29.9 & !is.na(input$bmi)),
bmi30_35 = +(input$bmi %in% 30:34.9 & !is.na(input$bmi)),
bmi35_40 = +(input$bmi %in% 35:39.9 & !is.na(input$bmi)),
bmige40 = +(input$bmi >= 40 & !is.na(input$bmi)),
bmimiss = +(input$bmi == "" | is.na(input$bmi)))
values <- c(demo.vals, bin.vals, num.vals, bmi) # Order sensitive!
# Adjustments
if(allow.age){
values$age[which(values$age < 30)] <- 30
values$age[which(values$age > 79)] <- 80
}
if(!allow.na){
vars <- c(smk.vars, bin.vars)
values[vars] <- sapply(vars,
function(x){
input[[x]] <- if(is.name(input[[x]])){
as.character(input[[x]])
}
replace(values[[x]],
which(is.na(input[[x]])),
NA)
},
USE.NAMES = TRUE,
simplify = FALSE)
}
values$exsmoker[which(values$smoker == 1)] <- 0
# Recentering
values$age[f.ind] <- values$age[f.ind] - 56.05801
values$age[m.ind] <- values$age[m.ind] - 51.59444
# browser()
values$nzdep[f.ind] <- values$nzdep[f.ind] - 2.994877
values$nzdep[m.ind] <- values$nzdep[m.ind] - 2.975732
values$sbp[f.ind] <- values$sbp[f.ind] - 128.6736
values$sbp[m.ind] <- values$sbp[m.ind] - 128.8637
values$tchdl[f.ind] <- values$tchdl[f.ind] - 3.715383
values$tchdl[m.ind] <- values$tchdl[m.ind] - 4.385853
# Interaction
values$int_age_diab <- ifelse(values$diabetes == 0, 0, values$age)
values$int_age_sbp <- values$age * values$sbp
values$int_sbp_bplt <- ifelse(values$bpl == 0, 0, values$sbp)
# Coefficients
fem.coeff <- list(age = 0.0734393,
maori = 0.4164622,
pacific = 0.2268597,
indian = 0.2086713,
asian = -0.2680559,
ex_smoke = 0.1444243,
cur_smoke = 0.6768396,
nzdep = 0.0957229,
diabetes = 0.4967444,
af = 0.9293084,
familyhx = 0.0645588,
lld = -0.0568366,
athrombi = 0.1393368,
bpl = 0.3487781,
sbp = 0.0176523,
tchdl = 0.1361335,
bmilt185 = 0.6277962,
bmi25_30 = 0.0018215,
bmi30_35 = -0.0169324,
bmi35_40 = 0.0343351,
bmige40 = 0.3196519,
bmimiss = 0.0213595,
int_age_diab = -0.0189779,
int_age_sbp = -0.000471,
int_sbp_bplt = -0.0054002)
male.coeff <- list(age = 0.0669484,
maori = 0.3166164,
pacific = 0.2217931,
indian = 0.3666816,
asian = -0.4131973,
ex_smoke = 0.0748648,
cur_smoke = 0.5317607,
nzdep = 0.0631146,
diabetes = 0.4107586,
af = 0.6250334,
familyhx = 0.1275721,
lld = -0.0256429,
athrombi = 0.0701999,
bpl = 0.2847596,
sbp = 0.0179827,
tchdl = 0.1296756,
bmilt185 = 0.5488212,
bmi25_30 = -0.033177,
bmi30_35 = -0.0025986,
bmi35_40 = 0.1202739,
bmige40 = 0.3799261,
bmimiss = -0.073928,
int_age_diab = -0.0124356,
int_age_sbp = -0.0004931,
int_sbp_bplt = -0.0049226)
value.score <- mapply(function(val, f.coeff, m.coeff){
effect <- rep(0, length(input$sex))
effect <- replace(effect, f.ind, val[f.ind] * f.coeff)
effect <- replace(effect, m.ind, val[m.ind] * m.coeff)
return(effect)
},
val = values,
f.coeff = fem.coeff,
m.coeff = male.coeff,
SIMPLIFY = F)
sum.score <- Reduce("+", value.score)
estimate <- rep(0, length(sum.score))
estimate <- replace(estimate, f.ind, 1 - 0.9845026 ^ exp(sum.score[f.ind]))
estimate <- replace(estimate, m.ind, 1 - 0.9712501 ^ exp(sum.score[m.ind]))
rounded.val <- as.numeric(formatC(round(estimate, dp),
format = 'f',
digits = dp))
if(length(ls(pattern = "inval.")) >= 1){
rounded.val <- replace(rounded.val,
unlist(mget(ls(pattern = "inval."))),
NA)
}
return(rounded.val)
}
billy-nz/PredictRiskScores documentation built on April 4, 2020, 6:23 p.m.
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Defines functions NoPriorCVDRisk_BMI
Documented in NoPriorCVDRisk_BMI
#' PREDICT CVD (2018.2) Risk Score for People Without Prior CVD
#'
#' \code{NoPriorCVDRisk_BMI} calculates the 5 year absolute risk of cardiovascular disease (CVD) for people without a history of atherosclerotic CVD.
#' In this version, BMI is used as a predictor. The outcome of interest is the 5-year risk of a non-fatal or fatal CVD event, including hospitalisation
#' for coronary heart disease, stroke or other cerebrovascular disease (including transient ischaemic attack), peripheral vascular disease and heart failure,
#' or cardiovascular death.
#'
#' @usage NoPriorCVDRisk_BMI(dat, sex, age, eth, nzdep, exsmoker, smoker, diabetes,
#' af, familyhx, sbp, tchdl, bmi, bpl, lld, athrombi, ...)
#'
#' @inheritParams NoPriorCVDRisk
#' @param bmi body mass index in kg/m^2
#'
#' @inherit NoPriorCVDRisk details
#'
#' @return
#' returns either a single 5-year CVD risk estimate, or a numeric vector of risk estimates if \code{dat} is provided.
#' Input values for each parameter must conform to the following convention:
#'
#' \item{sex}{label or encode as one of the following:
#' \itemize{
#' \item M, Male, 1
#' \item F, Female, 0
#' }}
#' \item{age}{numeric value for years of age between 20 and 110}
#' \item{eth}{label or encode as one of the following:
#' \itemize{
#' \item NZ European, European, NZEO, Euro, E, 1, 10, 11, or 12
#' \item Maori, NZMaori, NZ Maori, M, 2, or 21
#' \item Pacific, Pacific Islander, PI, P, 3, 30, 31, 32, 33, 34, 35, 36, or 37
#' \item Indian, Fijian Indian, South Asian, IN, I, or 43
#' \item Asian, Other Asian, SE Asian, East Asian, Chinese, ASN, A, 4, 40, 41, 42, or 44
#' \item note: Other Asian includes non-Indian South Asian
#' }}
#' \item{nzdep}{numeric value between 1 and 5}
#' \item{exsmoker}{label or encode as one of the following:
#' \itemize{
#' \item Y, Yes, Ex, Ex-smoker, Exsmoker, E, 1, T, TRUE
#' \item N, No, Non-smoker, Non, 0, F, FALSE
#' }}
#' \item{smoker}{label or encode as one of the following:
#' \itemize{
#' \item Y, Yes, Smoker, Current, S, 1, T, TRUE
#' \item N, No, Non-smoker, Non, 0, F, FALSE
#' }}
#' \item{diabetes,\cr af, hf}{label or encode as one of the following:
#' \itemize{
#' \item Y, Yes, 1, T, TRUE
#' \item N, No, 0, F, FALSE
#' }}
#' \item{bpl, lld,\cr athrombi}{label or encode as one of the following:
#' \itemize{
#' \item Y, Yes, 1, T, TRUE
#' \item N, No, 0, F, FALSE
#' }}
#' \item{sbp, tchdl}{numeric value of measured result. Note:
#' \itemize{
#' \item SBP and total:HDL values must be available
#' }}
#' \item{bmi}{numeric value of calculated BMI. If BMI is unknown, input as \code{NA}}
#' \item{...}{further arguments:
#' \itemize{
#' \item \code{dp} numeric value to set decimal place; default is 4
#' \item \code{allow.age} logical. Whether or not age range is extended outside of 30 - 74; default is TRUE. If set to FALSE, then \code{NA} is returned as risk estimate.
#' \item \code{allow.na} logical. Whether or not missing values for binary variables and smoking status are treated as 0; default is TRUE. If set to FALSE, then \code{NA} is returned as risk estimate.
#' }}
#'
#' @inheritSection NoPriorCVDRisk See Also
#'
#' @author
#' Billy Wu (R Developer) and Romana Pylypchuk (Principal Investigator)
#'
#' @references
#' New Zealand Ministry of Health: HISO 10071:2019 Cardiovascular Disease Risk Assessment Data Standard
#'
#' \href{https://www.health.govt.nz/publication/hiso-100712019-cardiovascular-disease-risk-assessment-data-standard}{HISO Document}
#'
#' @export
#' @examples
#' # As calculator (dataset not provided)
#' NoPriorCVDRisk_BMI(sex="F", age=65, eth="Indian", smoker=0, nzdep=5, diabetes=0,
#' af=0, familyhx=1, lld=1, athrombi=1, bpl=1, sbp=118, tchdl=3.3, bmi=32)
#'
#' NoPriorCVDRisk_BMI(sex=F, age=55, eth=IN, exsmoker=Y, smoker=0, nzdep=5, diabetes=T,
#' af=Y, familyhx=T, lld=1, athrombi=Y, bpl=T, sbp=120, tchdl=3.2, bmi=42)
#'
#' # As a vectoriser (dataset provided)
#' NoPriorCVDRisk_BMI(dat=DF, sex=sex, age=age, eth=ethnic_labels, smoker=smoking_status, nzdep=nzdep_quintiles,
#' diabetes=diab_status, af=af, familyhx=fam_hx, lld=lipidlowering, athrombi=antithrombics,
#' bpl=bplowering, sbp=systolic_bp, tchdl=tchdl_ratio, bmi=bmi)
#'
# --- Code ---
NoPriorCVDRisk_BMI <- function(dat, sex, age, eth, nzdep, exsmoker, smoker, diabetes, af, familyhx, sbp, tchdl, bmi, bpl, lld, athrombi,...){
# Params
demo.vars <- c("sex", "age", "eth", "nzdep")
smk.vars <- c("exsmoker", "smoker")
bin.vars <- c("diabetes", "af", "familyhx", "lld", "athrombi", "bpl")
num.vars <- c("sbp", "tchdl")
numNA.vars <- c("bmi")
# Calls
call <- gsub("()", "", match.call()[1])
is.table <- deparse(substitute(dat))!=""
input <- as.list(match.call()[-1])
if(length(list(...)) == 0){
dp <- 4
allow.age <- TRUE
allow.na <- TRUE
} else {
default <- setdiff(c("dp", "allow.age", "allow.na"), names(list(...)))
if(length(default) %in% 1:2){
lapply(default,
function(x){
if(x == "dp"){
val <- 4
} else if(x == "allow.na") {
val <- TRUE
} else {
val <- TRUE
}
assign(x, val, envir = parent.frame(2))
})
}
lapply(names(list(...)),
function(x)
assign(x, unlist(list(...)[x]),
envir = parent.frame(2)))
}
# ParamCheck
vars <- c(demo.vars, bin.vars, smk.vars, num.vars, numNA.vars)
ParamCheck(input, vars, call, is.table, allow.age, allow.na)
# Values
f.ind <- which(tolower(input$sex) %in% ok.female)
m.ind <- which(tolower(input$sex) %in% ok.male)
demo.vals <- list(age = input$age,
maori = +(tolower(input$eth) %in% ok.maori),
pacific = +(tolower(input$eth) %in% ok.pi),
indian = +(tolower(input$eth) %in% ok.indian),
asian = +(tolower(input$eth) %in% ok.asian),
exsmoker = +(tolower(input$exsmoker) %in% ok.exsmkr),
smoker = +(tolower(input$smoker) %in% ok.smoker),
nzdep = input$nzdep)
bin.vals <- sapply(bin.vars,
function(x){
+(tolower(input[[x]]) %in% ok.true)
},
USE.NAMES = TRUE,
simplify = FALSE)
num.vals <- sapply(num.vars,
function(x){
as.numeric(input[[x]])
},
USE.NAMES = TRUE,
simplify = FALSE)
bmi <- list(bmilt185 = +(input$bmi < 18.5 & !is.na(input$bmi)),
bmi25_30 = +(input$bmi %in% 25:29.9 & !is.na(input$bmi)),
bmi30_35 = +(input$bmi %in% 30:34.9 & !is.na(input$bmi)),
bmi35_40 = +(input$bmi %in% 35:39.9 & !is.na(input$bmi)),
bmige40 = +(input$bmi >= 40 & !is.na(input$bmi)),
bmimiss = +(input$bmi == "" | is.na(input$bmi)))
values <- c(demo.vals, bin.vals, num.vals, bmi) # Order sensitive!
# Adjustments
if(allow.age){
values$age[which(values$age < 30)] <- 30
values$age[which(values$age > 79)] <- 80
}
if(!allow.na){
vars <- c(smk.vars, bin.vars)
values[vars] <- sapply(vars,
function(x){
input[[x]] <- if(is.name(input[[x]])){
as.character(input[[x]])
}
replace(values[[x]],
which(is.na(input[[x]])),
NA)
},
USE.NAMES = TRUE,
simplify = FALSE)
}
values$exsmoker[which(values$smoker == 1)] <- 0
# Recentering
values$age[f.ind] <- values$age[f.ind] - 56.05801
values$age[m.ind] <- values$age[m.ind] - 51.59444
# browser()
values$nzdep[f.ind] <- values$nzdep[f.ind] - 2.994877
values$nzdep[m.ind] <- values$nzdep[m.ind] - 2.975732
values$sbp[f.ind] <- values$sbp[f.ind] - 128.6736
values$sbp[m.ind] <- values$sbp[m.ind] - 128.8637
values$tchdl[f.ind] <- values$tchdl[f.ind] - 3.715383
values$tchdl[m.ind] <- values$tchdl[m.ind] - 4.385853
# Interaction
values$int_age_diab <- ifelse(values$diabetes == 0, 0, values$age)
values$int_age_sbp <- values$age * values$sbp
values$int_sbp_bplt <- ifelse(values$bpl == 0, 0, values$sbp)
# Coefficients
fem.coeff <- list(age = 0.0734393,
maori = 0.4164622,
pacific = 0.2268597,
indian = 0.2086713,
asian = -0.2680559,
ex_smoke = 0.1444243,
cur_smoke = 0.6768396,
nzdep = 0.0957229,
diabetes = 0.4967444,
af = 0.9293084,
familyhx = 0.0645588,
lld = -0.0568366,
athrombi = 0.1393368,
bpl = 0.3487781,
sbp = 0.0176523,
tchdl = 0.1361335,
bmilt185 = 0.6277962,
bmi25_30 = 0.0018215,
bmi30_35 = -0.0169324,
bmi35_40 = 0.0343351,
bmige40 = 0.3196519,
bmimiss = 0.0213595,
int_age_diab = -0.0189779,
int_age_sbp = -0.000471,
int_sbp_bplt = -0.0054002)
male.coeff <- list(age = 0.0669484,
maori = 0.3166164,
pacific = 0.2217931,
indian = 0.3666816,
asian = -0.4131973,
ex_smoke = 0.0748648,
cur_smoke = 0.5317607,
nzdep = 0.0631146,
diabetes = 0.4107586,
af = 0.6250334,
familyhx = 0.1275721,
lld = -0.0256429,
athrombi = 0.0701999,
bpl = 0.2847596,
sbp = 0.0179827,
tchdl = 0.1296756,
bmilt185 = 0.5488212,
bmi25_30 = -0.033177,
bmi30_35 = -0.0025986,
bmi35_40 = 0.1202739,
bmige40 = 0.3799261,
bmimiss = -0.073928,
int_age_diab = -0.0124356,
int_age_sbp = -0.0004931,
int_sbp_bplt = -0.0049226)
value.score <- mapply(function(val, f.coeff, m.coeff){
effect <- rep(0, length(input$sex))
effect <- replace(effect, f.ind, val[f.ind] * f.coeff)
effect <- replace(effect, m.ind, val[m.ind] * m.coeff)
return(effect)
},
val = values,
f.coeff = fem.coeff,
m.coeff = male.coeff,
SIMPLIFY = F)
sum.score <- Reduce("+", value.score)
estimate <- rep(0, length(sum.score))
estimate <- replace(estimate, f.ind, 1 - 0.9845026 ^ exp(sum.score[f.ind]))
estimate <- replace(estimate, m.ind, 1 - 0.9712501 ^ exp(sum.score[m.ind]))
rounded.val <- as.numeric(formatC(round(estimate, dp),
format = 'f',
digits = dp))
if(length(ls(pattern = "inval.")) >= 1){
rounded.val <- replace(rounded.val,
unlist(mget(ls(pattern = "inval."))),
NA)
}
return(rounded.val)
}
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Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.