R/derive_param_framingham.R
In Roche-GSK/admiral: ADaM in R Asset Library
Defines functions
derive_param_framingham
Documented in
derive_param_framingham
#' Adds a Parameter for Framingham Heart Study Cardiovascular Disease
#' 10-Year Risk Score
#'
#' Adds a record for framingham score (FCVD101) for each by group
#' (e.g., subject and visit) where the source parameters are available.
#'
#' @param dataset
#' `r roxygen_param_dataset(expected_vars = c("by_vars"))`
#' `PARAMCD`, and `AVAL` are expected as well.
#'
#' The variable specified by `by_vars` and `PARAMCD` must be a unique key of
#' the input dataset after restricting it by the filter condition (`filter`
#' parameter) and to the parameters specified by `sysbp_code`, `chol_code`
#' and `hdl_code`.
#'
#' @param sysbp_code Systolic blood pressure parameter code
#'
#' The observations where `PARAMCD` equals the specified value are considered
#' as the systolic blood pressure assessments.
#'
#' @permitted [char_scalar]
#'
#' @param chol_code Total serum cholesterol code
#'
#' The observations where `PARAMCD` equals the specified value are considered
#' as the total cholesterol assessments. This must be measured in mg/dL.
#'
#' @permitted [char_scalar]
#'
#' @param cholhdl_code HDL serum cholesterol code
#'
#' The observations where `PARAMCD` equals the specified value are considered
#' as the HDL cholesterol assessments. This must be measured in mg/dL.
#'
#' @permitted [char_scalar]
#'
#' @param age Subject age
#'
#' A variable containing the subject's age.
#'
#' @permitted A numeric variable name that refers to a subject age
#' column of the input dataset
#'
#' @param sex Subject sex
#'
#' A variable containing the subject's sex.
#'
#' @permitted A character variable name that refers to a subject sex
#' column of the input dataset
#'
#' @param smokefl Smoking status flag
#'
#' A flag indicating smoking status.
#'
#' @permitted A character variable name that refers to a smoking status
#' column of the input dataset.
#'
#' @param diabetfl Diabetic flag
#'
#' A flag indicating diabetic status.
#'
#' @permitted A character variable name that refers to a diabetic
#' status column of the input dataset
#'
#' @param trthypfl Treated with hypertension medication flag
#'
#' A flag indicating if a subject was treated with hypertension medication.
#'
#' @permitted A character variable name that refers to a column that
#' indicates whether a subject is treated for high blood
#' pressure
#'
#' @inheritParams derive_param_qtc
#'
#' @details
#' The values of `age`, `sex`, `smokefl`, `diabetfl` and `trthypfl` will be
#' added to the `by_vars` list.
#' The predicted probability of having cardiovascular disease (CVD)
#' within 10-years according to Framingham formula. See AHA Journal article
#' General Cardiovascular Risk Profile for Use in Primary Care for reference.
#'
#' \strong{For Women:}
#'
#' \tabular{rr}{
#' \strong{Factor} \tab \strong{Amount} \cr
#' Age \tab 2.32888 \cr
#' Total Chol \tab 1.20904 \cr
#' HDL Chol \tab -0.70833 \cr
#' Sys BP \tab 2.76157 \cr
#' Sys BP + Hypertension Meds \tab 2.82263 \cr
#' Smoker \tab 0.52873 \cr
#' Non-Smoker \tab 0 \cr
#' Diabetic \tab 0.69154 \cr
#' Not Diabetic \tab 0 \cr
#' Average Risk \tab 26.1931 \cr
#' Risk Period \tab 0.95012 \cr
#' }
#'
#' \strong{For Men:}
#'
#' \tabular{rr}{
#' \strong{Factor} \tab \strong{Amount} \cr
#' Age \tab 3.06117 \cr
#' Total Chol \tab 1.12370 \cr
#' HDL Chol \tab -0.93263 \cr
#' Sys BP \tab 1.93303 \cr
#' Sys BP + Hypertension Meds \tab 2.99881 \cr
#' Smoker \tab .65451 \cr
#' Non-Smoker \tab 0 \cr
#' Diabetic \tab 0.57367 \cr
#' Not Diabetic \tab 0 \cr
#' Average Risk \tab 23.9802 \cr
#' Risk Period \tab 0.88936 \cr
#' }
#'
#' \strong{The equation for calculating risk:}
#'
#' \deqn{RiskFactors = (log(Age) * AgeFactor)
#' + (log(TotalChol) * TotalCholFactor)
#' + (log(CholHDL) * CholHDLFactor) \\
#' + (log(SysBP) * SysBPFactor) + Smoker
#' + Diabetes Present - AvgRisk}
#'
#' \deqn{Risk = 100 * (1 - RiskPeriodFactor^{RiskFactors})}
#'
#' @return The input dataset with the new parameter added
#'
#' @keywords der_prm_bds_findings
#' @family der_prm_bds_findings
#'
#' @export
#'
#' @seealso [compute_framingham()]
#'
#' @examples
#' library(tibble)
#'
#' adcvrisk <- tribble(
#' ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~AVALU,
#' ~VISIT, ~AGE, ~SEX, ~SMOKEFL, ~DIABETFL, ~TRTHYPFL,
#' "01-701-1015", "SYSBP", "Systolic Blood Pressure (mmHg)", 121,
#' "mmHg", "BASELINE", 44, "F", "N", "N", "N",
#' "01-701-1015", "SYSBP", "Systolic Blood Pressure (mmHg)", 115,
#' "mmHg", "WEEK 2", 44, "F", "N", "N", "Y",
#' "01-701-1015", "CHOL", "Total Cholesterol (mg/dL)", 216.16,
#' "mg/dL", "BASELINE", 44, "F", "N", "N", "N",
#' "01-701-1015", "CHOL", "Total Cholesterol (mg/dL)", 210.78,
#' "mg/dL", "WEEK 2", 44, "F", "N", "N", "Y",
#' "01-701-1015", "CHOLHDL", "Cholesterol/HDL-Cholesterol (mg/dL)", 54.91,
#' "mg/dL", "BASELINE", 44, "F", "N", "N", "N",
#' "01-701-1015", "CHOLHDL", "Cholesterol/HDL-Cholesterol (mg/dL)", 26.72,
#' "mg/dL", "WEEK 2", 44, "F", "N", "N", "Y",
#' "01-701-1028", "SYSBP", "Systolic Blood Pressure (mmHg)", 119,
#' "mmHg", "BASELINE", 55, "M", "Y", "Y", "Y",
#' "01-701-1028", "SYSBP", "Systolic Blood Pressure (mmHg)", 101,
#' "mmHg", "WEEK 2", 55, "M", "Y", "Y", "Y",
#' "01-701-1028", "CHOL", "Total Cholesterol (mg/dL)", 292.01,
#' "mg/dL", "BASELINE", 55, "M", "Y", "Y", "Y",
#' "01-701-1028", "CHOL", "Total Cholesterol (mg/dL)", 246.73,
#' "mg/dL", "WEEK 2", 55, "M", "Y", "Y", "Y",
#' "01-701-1028", "CHOLHDL", "Cholesterol/HDL-Cholesterol (mg/dL)", 65.55,
#' "mg/dL", "BASELINE", 55, "M", "Y", "Y", "Y",
#' "01-701-1028", "CHOLHDL", "Cholesterol/HDL-Cholesterol (mg/dL)", 44.62,
#' "mg/dL", "WEEK 2", 55, "M", "Y", "Y", "Y"
#' )
#'
#'
#' adcvrisk %>%
#' derive_param_framingham(
#' by_vars = exprs(USUBJID, VISIT),
#' set_values_to = exprs(
#' PARAMCD = "FCVD101",
#' PARAM = "FCVD1-Framingham CVD 10-Year Risk Score (%)"
#' ),
#' get_unit_expr = AVALU
#' )
#'
#' derive_param_framingham(
#' adcvrisk,
#' by_vars = exprs(USUBJID, VISIT),
#' set_values_to = exprs(
#' PARAMCD = "FCVD101",
#' PARAM = "FCVD1-Framingham CVD 10-Year Risk Score (%)"
#' ),
#' get_unit_expr = extract_unit(PARAM)
#' )
derive_param_framingham <- function(dataset,
by_vars,
set_values_to = exprs(PARAMCD = "FCVD101"),
sysbp_code = "SYSBP",
chol_code = "CHOL",
cholhdl_code = "CHOLHDL",
age = AGE,
sex = SEX,
smokefl = SMOKEFL,
diabetfl = DIABETFL,
trthypfl = TRTHYPFL,
get_unit_expr,
filter = NULL) {
assert_vars(by_vars)
age <- assert_symbol(enexpr(age))
sex <- assert_symbol(enexpr(sex))
smokefl <- assert_symbol(enexpr(smokefl))
diabetfl <- assert_symbol(enexpr(diabetfl))
trthypfl <- assert_symbol(enexpr(trthypfl))
assert_data_frame(
dataset,
required_vars = expr_c(
by_vars,
exprs(PARAMCD, AVAL),
age,
sex,
smokefl,
diabetfl,
trthypfl
)
)
assert_varval_list(set_values_to, required_elements = "PARAMCD")
assert_param_does_not_exist(dataset, set_values_to$PARAMCD)
assert_character_scalar(sysbp_code)
assert_character_scalar(chol_code)
assert_character_scalar(cholhdl_code)
filter <- assert_filter_cond(enexpr(filter), optional = TRUE)
get_unit_expr <- assert_expr(enexpr(get_unit_expr))
assert_unit(
dataset,
sysbp_code,
required_unit = "mmHg",
get_unit_expr = !!get_unit_expr
)
assert_unit(
dataset,
chol_code,
required_unit = "mg/dL",
get_unit_expr = !!get_unit_expr
)
assert_unit(
dataset,
cholhdl_code,
required_unit = "mg/dL",
get_unit_expr = !!get_unit_expr
)
analysis_value <- expr(
compute_framingham(
sysbp = !!sym(paste0("AVAL.", sysbp_code)),
chol = !!sym(paste0("AVAL.", chol_code)),
cholhdl = !!sym(paste0("AVAL.", cholhdl_code)),
age = !!age,
sex = !!sex,
smokefl = !!smokefl,
diabetfl = !!diabetfl,
trthypfl = !!trthypfl
)
)
derive_param_computed(
dataset,
filter = !!filter,
parameters = c(sysbp_code, chol_code, cholhdl_code),
by_vars = expr_c(
by_vars,
age,
sex,
smokefl,
diabetfl,
trthypfl
),
set_values_to = exprs(
AVAL = !!analysis_value,
!!!set_values_to
)
)
}
Roche-GSK/admiral documentation built on April 14, 2025, 12:36 p.m.
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Defines functions derive_param_framingham
Documented in derive_param_framingham
#' Adds a Parameter for Framingham Heart Study Cardiovascular Disease
#' 10-Year Risk Score
#'
#' Adds a record for framingham score (FCVD101) for each by group
#' (e.g., subject and visit) where the source parameters are available.
#'
#' @param dataset
#' `r roxygen_param_dataset(expected_vars = c("by_vars"))`
#' `PARAMCD`, and `AVAL` are expected as well.
#'
#' The variable specified by `by_vars` and `PARAMCD` must be a unique key of
#' the input dataset after restricting it by the filter condition (`filter`
#' parameter) and to the parameters specified by `sysbp_code`, `chol_code`
#' and `hdl_code`.
#'
#' @param sysbp_code Systolic blood pressure parameter code
#'
#' The observations where `PARAMCD` equals the specified value are considered
#' as the systolic blood pressure assessments.
#'
#' @permitted [char_scalar]
#'
#' @param chol_code Total serum cholesterol code
#'
#' The observations where `PARAMCD` equals the specified value are considered
#' as the total cholesterol assessments. This must be measured in mg/dL.
#'
#' @permitted [char_scalar]
#'
#' @param cholhdl_code HDL serum cholesterol code
#'
#' The observations where `PARAMCD` equals the specified value are considered
#' as the HDL cholesterol assessments. This must be measured in mg/dL.
#'
#' @permitted [char_scalar]
#'
#' @param age Subject age
#'
#' A variable containing the subject's age.
#'
#' @permitted A numeric variable name that refers to a subject age
#' column of the input dataset
#'
#' @param sex Subject sex
#'
#' A variable containing the subject's sex.
#'
#' @permitted A character variable name that refers to a subject sex
#' column of the input dataset
#'
#' @param smokefl Smoking status flag
#'
#' A flag indicating smoking status.
#'
#' @permitted A character variable name that refers to a smoking status
#' column of the input dataset.
#'
#' @param diabetfl Diabetic flag
#'
#' A flag indicating diabetic status.
#'
#' @permitted A character variable name that refers to a diabetic
#' status column of the input dataset
#'
#' @param trthypfl Treated with hypertension medication flag
#'
#' A flag indicating if a subject was treated with hypertension medication.
#'
#' @permitted A character variable name that refers to a column that
#' indicates whether a subject is treated for high blood
#' pressure
#'
#' @inheritParams derive_param_qtc
#'
#' @details
#' The values of `age`, `sex`, `smokefl`, `diabetfl` and `trthypfl` will be
#' added to the `by_vars` list.
#' The predicted probability of having cardiovascular disease (CVD)
#' within 10-years according to Framingham formula. See AHA Journal article
#' General Cardiovascular Risk Profile for Use in Primary Care for reference.
#'
#' \strong{For Women:}
#'
#' \tabular{rr}{
#' \strong{Factor} \tab \strong{Amount} \cr
#' Age \tab 2.32888 \cr
#' Total Chol \tab 1.20904 \cr
#' HDL Chol \tab -0.70833 \cr
#' Sys BP \tab 2.76157 \cr
#' Sys BP + Hypertension Meds \tab 2.82263 \cr
#' Smoker \tab 0.52873 \cr
#' Non-Smoker \tab 0 \cr
#' Diabetic \tab 0.69154 \cr
#' Not Diabetic \tab 0 \cr
#' Average Risk \tab 26.1931 \cr
#' Risk Period \tab 0.95012 \cr
#' }
#'
#' \strong{For Men:}
#'
#' \tabular{rr}{
#' \strong{Factor} \tab \strong{Amount} \cr
#' Age \tab 3.06117 \cr
#' Total Chol \tab 1.12370 \cr
#' HDL Chol \tab -0.93263 \cr
#' Sys BP \tab 1.93303 \cr
#' Sys BP + Hypertension Meds \tab 2.99881 \cr
#' Smoker \tab .65451 \cr
#' Non-Smoker \tab 0 \cr
#' Diabetic \tab 0.57367 \cr
#' Not Diabetic \tab 0 \cr
#' Average Risk \tab 23.9802 \cr
#' Risk Period \tab 0.88936 \cr
#' }
#'
#' \strong{The equation for calculating risk:}
#'
#' \deqn{RiskFactors = (log(Age) * AgeFactor)
#' + (log(TotalChol) * TotalCholFactor)
#' + (log(CholHDL) * CholHDLFactor) \\
#' + (log(SysBP) * SysBPFactor) + Smoker
#' + Diabetes Present - AvgRisk}
#'
#' \deqn{Risk = 100 * (1 - RiskPeriodFactor^{RiskFactors})}
#'
#' @return The input dataset with the new parameter added
#'
#' @keywords der_prm_bds_findings
#' @family der_prm_bds_findings
#'
#' @export
#'
#' @seealso [compute_framingham()]
#'
#' @examples
#' library(tibble)
#'
#' adcvrisk <- tribble(
#' ~USUBJID, ~PARAMCD, ~PARAM, ~AVAL, ~AVALU,
#' ~VISIT, ~AGE, ~SEX, ~SMOKEFL, ~DIABETFL, ~TRTHYPFL,
#' "01-701-1015", "SYSBP", "Systolic Blood Pressure (mmHg)", 121,
#' "mmHg", "BASELINE", 44, "F", "N", "N", "N",
#' "01-701-1015", "SYSBP", "Systolic Blood Pressure (mmHg)", 115,
#' "mmHg", "WEEK 2", 44, "F", "N", "N", "Y",
#' "01-701-1015", "CHOL", "Total Cholesterol (mg/dL)", 216.16,
#' "mg/dL", "BASELINE", 44, "F", "N", "N", "N",
#' "01-701-1015", "CHOL", "Total Cholesterol (mg/dL)", 210.78,
#' "mg/dL", "WEEK 2", 44, "F", "N", "N", "Y",
#' "01-701-1015", "CHOLHDL", "Cholesterol/HDL-Cholesterol (mg/dL)", 54.91,
#' "mg/dL", "BASELINE", 44, "F", "N", "N", "N",
#' "01-701-1015", "CHOLHDL", "Cholesterol/HDL-Cholesterol (mg/dL)", 26.72,
#' "mg/dL", "WEEK 2", 44, "F", "N", "N", "Y",
#' "01-701-1028", "SYSBP", "Systolic Blood Pressure (mmHg)", 119,
#' "mmHg", "BASELINE", 55, "M", "Y", "Y", "Y",
#' "01-701-1028", "SYSBP", "Systolic Blood Pressure (mmHg)", 101,
#' "mmHg", "WEEK 2", 55, "M", "Y", "Y", "Y",
#' "01-701-1028", "CHOL", "Total Cholesterol (mg/dL)", 292.01,
#' "mg/dL", "BASELINE", 55, "M", "Y", "Y", "Y",
#' "01-701-1028", "CHOL", "Total Cholesterol (mg/dL)", 246.73,
#' "mg/dL", "WEEK 2", 55, "M", "Y", "Y", "Y",
#' "01-701-1028", "CHOLHDL", "Cholesterol/HDL-Cholesterol (mg/dL)", 65.55,
#' "mg/dL", "BASELINE", 55, "M", "Y", "Y", "Y",
#' "01-701-1028", "CHOLHDL", "Cholesterol/HDL-Cholesterol (mg/dL)", 44.62,
#' "mg/dL", "WEEK 2", 55, "M", "Y", "Y", "Y"
#' )
#'
#'
#' adcvrisk %>%
#' derive_param_framingham(
#' by_vars = exprs(USUBJID, VISIT),
#' set_values_to = exprs(
#' PARAMCD = "FCVD101",
#' PARAM = "FCVD1-Framingham CVD 10-Year Risk Score (%)"
#' ),
#' get_unit_expr = AVALU
#' )
#'
#' derive_param_framingham(
#' adcvrisk,
#' by_vars = exprs(USUBJID, VISIT),
#' set_values_to = exprs(
#' PARAMCD = "FCVD101",
#' PARAM = "FCVD1-Framingham CVD 10-Year Risk Score (%)"
#' ),
#' get_unit_expr = extract_unit(PARAM)
#' )
derive_param_framingham <- function(dataset,
by_vars,
set_values_to = exprs(PARAMCD = "FCVD101"),
sysbp_code = "SYSBP",
chol_code = "CHOL",
cholhdl_code = "CHOLHDL",
age = AGE,
sex = SEX,
smokefl = SMOKEFL,
diabetfl = DIABETFL,
trthypfl = TRTHYPFL,
get_unit_expr,
filter = NULL) {
assert_vars(by_vars)
age <- assert_symbol(enexpr(age))
sex <- assert_symbol(enexpr(sex))
smokefl <- assert_symbol(enexpr(smokefl))
diabetfl <- assert_symbol(enexpr(diabetfl))
trthypfl <- assert_symbol(enexpr(trthypfl))
assert_data_frame(
dataset,
required_vars = expr_c(
by_vars,
exprs(PARAMCD, AVAL),
age,
sex,
smokefl,
diabetfl,
trthypfl
)
)
assert_varval_list(set_values_to, required_elements = "PARAMCD")
assert_param_does_not_exist(dataset, set_values_to$PARAMCD)
assert_character_scalar(sysbp_code)
assert_character_scalar(chol_code)
assert_character_scalar(cholhdl_code)
filter <- assert_filter_cond(enexpr(filter), optional = TRUE)
get_unit_expr <- assert_expr(enexpr(get_unit_expr))
assert_unit(
dataset,
sysbp_code,
required_unit = "mmHg",
get_unit_expr = !!get_unit_expr
)
assert_unit(
dataset,
chol_code,
required_unit = "mg/dL",
get_unit_expr = !!get_unit_expr
)
assert_unit(
dataset,
cholhdl_code,
required_unit = "mg/dL",
get_unit_expr = !!get_unit_expr
)
analysis_value <- expr(
compute_framingham(
sysbp = !!sym(paste0("AVAL.", sysbp_code)),
chol = !!sym(paste0("AVAL.", chol_code)),
cholhdl = !!sym(paste0("AVAL.", cholhdl_code)),
age = !!age,
sex = !!sex,
smokefl = !!smokefl,
diabetfl = !!diabetfl,
trthypfl = !!trthypfl
)
)
derive_param_computed(
dataset,
filter = !!filter,
parameters = c(sysbp_code, chol_code, cholhdl_code),
by_vars = expr_c(
by_vars,
age,
sex,
smokefl,
diabetfl,
trthypfl
),
set_values_to = exprs(
AVAL = !!analysis_value,
!!!set_values_to
)
)
}
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