Package 'scicalc'

Description

Converts relative eGFR (normalized to 1.73 m²) to absolute eGFR using the patient's actual body surface area.

Usage

aegfr(egfr, bsa)

Arguments

Details

Absolute eGFR is calculated as:

$aGFR = \frac{eGFR \cdot BSA}{1.73}$

where:

• $eGFR$ = relative eGFR (mL/min/1.73m²)

• $BSA$ = body surface area (m²)

Value

Absolute eGFR (mL/min)

See Also

Other renal_function: ckdepi_2009_egfr(), ckdepi_2021_egfr(), ckdepi_2021_egfr_cystatin(), crcl(), egfr(), mdrd_egfr(), rfc(), schwartz_egfr()

Examples

aegfr(90, 1.9)

df <- data.frame(
  ID = c(1, 2, 3, 4),
  EGFR = c(80, 95, 70, 60),
  BSA = c(1.60, 1.85, 1.75, 2.00)
)

df <- df %>%
  dplyr::group_by(ID) %>%
  dplyr::mutate(AEGFR = aegfr(EGFR, BSA))
df

Description

Categorizes subjects into FDA-defined age groups for pediatric and geriatric drug development studies.

Usage

agec(age)

Arguments

Details

FDA age categories:

• 1: Neonate: 0 to <28 days

• 2: Infant: 28 days to <2 years

• 3: Child: 2 to <12 years

• 4: Adolescent: 12 to <18 years

• 5: Adult: 18 to <65 years

• 6: Elderly Adult: ≥65 years

Value

Integer vector of age categories (1-6). Returns -999 for missing values. Includes a category_standard attribute set to "FDA".

References

Pediatric Drug Development: Regulatory Considerations — Complying With the Pediatric Research Equity Act and Qualifying for Pediatric Exclusivity Under the Best Pharmaceuticals for Children Act Guidance for Industry

Guideline for Industry Studies in Support of Special Populations: Geriatrics

Examples

age_cat <- agec(24)

df <- data.frame(
  ID = 1:12,
  AGE = c(0.07, 28 / 365, 0.25, 1, 2, 4, 12, 16, 18, 24, 65, 70)
)
dplyr::mutate(df, AGEC = agec(AGE))

Description

Calculates adjusted ideal body weight, which accounts for excess body weight in obese patients.

Usage

aibw(
  weight,
  height,
  sexf,
  age,
  allow_ibw_lt_intercept = TRUE,
  allow_tbw_lt_ibw = TRUE
)

Arguments

Details

Adjusted ideal body weight is calculated as:

$AIBW = IBW + 0.4 \cdot (TBW - IBW)$

where:

• $IBW$ = ideal body weight (kg)

• $TBW$ = total (actual) body weight (kg)

Value

adjusted ideal body weight (kg)

See Also

Other body_composition: bmi(), bmic(), bsa(), dubois_bsa(), ibw(), mosteller_bsa()

Examples

df <- data.frame(
  ID = 1:6,
  HEIGHT = c(160, 170, 175, 165, 180, 150),
  WEIGHT = c(53, 71, 78, 55, 72, 43),
  SEX = c(1, 0, 0, 1, 0, 1),
  AGE = c(18, 27, 34, 33, 29, 30)
)
df <- dplyr::mutate(df, AIBW = aibw(WEIGHT, HEIGHT, SEX, AGE))
df

Description

Calculate Body Mass Index

Usage

bmi(weight, height)

Arguments

Details

BMI is calculated using the formula:

$BMI = \frac{W}{(H/100)^2}$

where:

• $W$ = weight (kg)

• $H$ = height (cm)

Value

the BMI value (kg/m^2)

See Also

Other body_composition: aibw(), bmic(), bsa(), dubois_bsa(), ibw(), mosteller_bsa()

Examples

b <- bmi(80.56, 167)

df <- data.frame(
  "WT" = c(80.56, 71.53, 81.04, 70.17),
  "HT" = c(167, 161, 163, 164)
)
df <- dplyr::mutate(df, bmi = bmi(WT, HT))
df

Description

Categorizes individuals based on their Body Mass Index (BMI) according to standard WHO obesity classification criteria. Validates age to ensure appropriate application of adult BMI categories.

Usage

bmic(bmi, age)

Arguments

Details

BMI Categories:

• 1: Underweight: BMI < 18.5 kg/m²

• 2: Normal weight: BMI 18.5 to < 25 kg/m²

• 3: Overweight: BMI 25 to < 30 kg/m²

• 4: Obese: BMI ≥ 30 kg/m²

Age Considerations: The function will warn if any individuals are under 18 years old, as standard adult BMI categories may not be appropriate for children and adolescents.

Value

Integer vector of obesity categories (1-4). Returns -999 for missing BMI values.

References

World Health Organization. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight

See Also

bmi for calculating BMI from weight and height, agec for age categorization

Other body_composition: aibw(), bmi(), bsa(), dubois_bsa(), ibw(), mosteller_bsa()

Examples

patients <- data.frame(
  ID = 1:6,
  AGE = c(25, 45, 17, 55, 30, 65),
  WEIGHT = c(60, 80, 70, 90, 75, 85),
  HEIGHT = c(165, 175, 170, 180, 160, 175)
)

dplyr::mutate(
 patients,
  BMI = bmi(WEIGHT, HEIGHT),
  BMIC = bmic(BMI, AGE)
)

Description

Calculate Body Surface Area

Usage

bsa(weight, height, method = "Dubois")

Arguments

Value

bsa (m^2)

See Also

Other body_composition: aibw(), bmi(), bmic(), dubois_bsa(), ibw(), mosteller_bsa()

Examples

bsa(70, 170)
bsa(70, 170, method = "Mosteller")
bsa(70, 170, method = "Dubois")

Description

Categorize Continuous Variable into Quantile Bins

Usage

categorize(continuous_var, nbins = 4, units = "", type = 7, digits = 1)

Arguments

Value

a vector of categorized data as factor

Examples

x <- rnorm(1000, mean = 10, sd = 5)
categorize(x, nbins = 5)

Description

Check for Unique Units per Parameter

Usage

check_for_unique_units(params, units)

Arguments

Value

a boolean

See Also

Other unit_checking: get_unique_units_df()

Examples

df <- data.frame(
  PARAM = c(
    "ALB","ALT","AST","CR","TBIL",
    "ALB","CR","TBIL","ALT","AST"),
  UNIT = c(
    "g/L","U/L","U/L","umol/L","umol/L",
    "U/L","μmol/L","μmol/L","IU/L","IU/L")
)
check_for_unique_units(df$PARAM, df$UNIT)

Description

Calculate eGFR Using CKD-EPI 2009 Equation

Usage

ckdepi_2009_egfr(sexf, raceb, age, creat)

Arguments

Details

The CKD-EPI 2009 equation:

$eGFR = 141 \cdot \min(S_{cr}/\kappa, 1)^\alpha \cdot \max(S_{cr}/\kappa, 1)^{-1.209} \cdot 0.993^A \cdot 1.018^F \cdot 1.159^B$

where:

• $S_{cr}$ = serum creatinine (mg/dL)

• $\kappa$ = 0.7 (female) or 0.9 (male)

• $\alpha$ = -0.329 (female) or -0.411 (male)

• $A$ = age (years)

• $F$ = 1 (female) or 0 (male)

• $B$ = 1 (Black) or 0 (other)

Value

the eGFR value (mL/min/1.73m2)

See Also

Other renal_function: aegfr(), ckdepi_2021_egfr(), ckdepi_2021_egfr_cystatin(), crcl(), egfr(), mdrd_egfr(), rfc(), schwartz_egfr()

Examples

e <- ckdepi_2009_egfr(TRUE, TRUE, 24, 1)

df <- data.frame(
  "SEXF" = c(TRUE, FALSE, TRUE, FALSE),
  "RACEB" = c(FALSE, FALSE, TRUE, FALSE),
  "AGE" = c(24, 24, 23, 24),
  "CREAT" = c(1, 1, 2, 1)
)
df <- dplyr::mutate(df, egfr = ckdepi_2009_egfr(SEXF, RACEB, AGE, CREAT))
df

Description

Calculate eGFR Using CKD-EPI 2021 Creatinine Equation

Usage

ckdepi_2021_egfr(sexf, age, creat)

Arguments

Details

The CKD-EPI 2021 creatinine equation (race-free):

$eGFR = 142 \cdot \min(S_{cr}/\kappa, 1)^\alpha \cdot \max(S_{cr}/\kappa, 1)^{-1.2} \cdot 0.9938^A \cdot 1.012^F$

where:

• $S_{cr}$ = serum creatinine (mg/dL)

• $\kappa$ = 0.7 (female) or 0.9 (male)

• $\alpha$ = -0.241 (female) or -0.302 (male)

• $A$ = age (years)

• $F$ = 1 (female) or 0 (male)

Value

the eGFR value (mL/min/1.73m2)

See Also

Other renal_function: aegfr(), ckdepi_2009_egfr(), ckdepi_2021_egfr_cystatin(), crcl(), egfr(), mdrd_egfr(), rfc(), schwartz_egfr()

Examples

e <- ckdepi_2021_egfr(TRUE, 24, 1)

df <- data.frame(
  "SEXF" = c(TRUE, FALSE, TRUE, FALSE),
  "RACEB" = c(FALSE, FALSE, TRUE, FALSE),
  "AGE" = c(24, 24, 23, 24),
  "CREAT" = c(1, 1, 2, 1)
)
df <- dplyr::mutate(df, egfr = ckdepi_2021_egfr(SEXF, AGE, CREAT))
df

Description

Calculate eGFR Using CKD-EPI 2021 Cystatin Equation

Usage

ckdepi_2021_egfr_cystatin(sexf, age, creat, cystc)

Arguments

Details

The CKD-EPI 2021 creatinine-cystatin equation:

$eGFR = 135 \cdot \min(S_{cr}/\kappa, 1)^\alpha \cdot \max(S_{cr}/\kappa, 1)^{-0.544} \cdot \min(S_{cys}/0.8, 1)^{-0.323} \\ \cdot \max(S_{cys}/0.8, 1)^{-0.778} \cdot 0.9961^A \cdot 0.963^F$

where:

• $S_{cr}$ = serum creatinine (mg/dL)

• $S_{cys}$ = serum cystatin C (mg/L)

• $\kappa$ = 0.7 (female) or 0.9 (male)

• $\alpha$ = -0.219 (female) or -0.144 (male)

• $A$ = age (years)

• $F$ = 1 (female) or 0 (male)

Value

eGFR in mL/min/1.73 m^2

See Also

Other renal_function: aegfr(), ckdepi_2009_egfr(), ckdepi_2021_egfr(), crcl(), egfr(), mdrd_egfr(), rfc(), schwartz_egfr()

Examples

e <- ckdepi_2021_egfr_cystatin(TRUE, 24, 1, 2)

df <- data.frame(
  "SEXF" = c(TRUE, FALSE, TRUE, FALSE),
  "RACEB" = c(FALSE, FALSE, TRUE, FALSE),
  "AGE" = c(24, 24, 23, 24),
  "CREAT" = c(1, 1, 2, 1),
  "CYSTC" = c(0.4, 0.8, 1, 2)
)
df <- dplyr::mutate(df, egfr = ckdepi_2021_egfr_cystatin(SEXF, AGE, CREAT, CYSTC))
df

Description

Convert Albumin Concentration Units

Usage

convert_alb(alb)

Arguments

Value

Albumin concentration (g/dL)

See Also

Other unit_conversion: convert_bili(), convert_creat()

Examples

convert_alb(40)

df <- data.frame(
  ID = c(1, 2, 3, 4),
  ALB = c(35, 40, 28, 45)
)

df <- df %>%
  dplyr::group_by(ID) %>%
  dplyr::mutate(ALBBL = convert_alb(ALB))
df

Description

Convert Bilirubin Concentration Units

Usage

convert_bili(bili)

Arguments

Value

Bilirubin concentration (mg/dL)

See Also

Other unit_conversion: convert_alb(), convert_creat()

Examples

convert_bili(17.1) # ≈ 1 mg/dL

df <- data.frame(
  ID = c(1, 2, 3, 4),
  BILI = c(10, 15, 25, 40)
)

df <- df %>%
  dplyr::group_by(ID) %>%
  dplyr::mutate(BILIBL = convert_bili(BILI))
df

Description

Convert Serum Creatinine Concentration Units

Usage

convert_creat(creat)

Arguments

Value

Serum Creatinine concentration (mg/dL)

See Also

Other unit_conversion: convert_alb(), convert_bili()

Examples

convert_creat(88.42) # ≈ 1 mg/dL

df <- data.frame(
  ID = c(1, 2, 3, 4),
  CREAT = c(70, 90, 110, 130)
)

df <- df %>%
  dplyr::group_by(ID) %>%
  dplyr::mutate(CREATBL = convert_creat(CREAT))
df

Description

Computes pairwise correlations between numeric columns and returns results in a tidy long format, sorted by absolute correlation.

Usage

cor_df(data, columns = NULL, use = "complete.obs", method = "pearson")

Arguments

Value

A tibble with columns:

name1, name2

Variable pair names (lexicographically ordered)

CORR

Correlation coefficient

ABSCORR

Absolute correlation coefficient

Results are sorted by ABSCORR in descending order.

See Also

Other statistics: cv(), geom_cv(), geom_mean(), geom_sd()

Examples

# Create sample data
set.seed(123)
df <- data.frame(
  A = rnorm(100, 5, 2),
  B = rnorm(100, 10, 3),
  C = rnorm(100, 15, 1),
  D = letters[1:100] # non-numeric
)
df$B <- df$A * 0.8 + rnorm(100, 0, 1) # Create some correlation

# All numeric columns
cor_df(df)

# Specific columns
cor_df(df, columns = c("A", "B", "C"))

Description

Calculate Creatinine Clearance Using Cockcroft-Gault Equation

Usage

crcl(sexf, age, creat, weight)

Arguments

Details

The Cockcroft-Gault equation:

$CrCl = F \cdot \frac{(140 - A) \cdot W}{72 \cdot S_{cr}}$

where:

• $A$ = age (years)

• $W$ = weight (kg)

• $S_{cr}$ = serum creatinine (mg/dL)

• $F$ = 0.85 (female) or 1 (male)

Value

CrCl (mL/min)

See Also

Other renal_function: aegfr(), ckdepi_2009_egfr(), ckdepi_2021_egfr(), ckdepi_2021_egfr_cystatin(), egfr(), mdrd_egfr(), rfc(), schwartz_egfr()

Examples

crcl(FALSE, 20, 10, 70)

df <- data.frame(
  "ID" = c(1, 1, 1, 1, 2, 2, 2, 2),
  "SEX" = c("F", "F", "F", "F", "M", "M", "M", "M"),
  "RACE" = c("WHITE", "WHITE", "WHITE", "WHITE", "BLACK", "BLACK", "BLACK", "BLACK"),
  "AGE" = c(24, 24, 24, 24, 22, 22, 22, 22),
  "CREAT" = c(1, 1, 1, 1, 4, 4, 4, 4),
  "WEIGHT" = c(70, 70, 70, 70, 65, 65, 65, 65)
)

df <- df %>%
  dplyr::group_by(ID) %>%
  dplyr::mutate(CRCL = crcl(is_female(SEX), AGE, CREAT, WEIGHT))
df

Description

Calculate Coefficient of Variation

Usage

cv(x, na.rm = FALSE)

Arguments

Details

The coefficient of variation is calculated as:

$CV = \frac{\sigma}{\mu}$

where:

• $\sigma$ = standard deviation

• $\mu$ = mean

Value

CV of x. Standard deviation divided by mean. If you want % you'll need to multiply by 100

See Also

Other statistics: cor_df(), geom_cv(), geom_mean(), geom_sd()

Examples

cv(c(1, 2, 1, 1, 2, 1, 2, 3))

Description

Calculate Body Surface Area Using Du Bois Equation

Usage

dubois_bsa(weight, height)

Arguments

Details

The Du Bois equation for BSA:

$BSA = 0.007184 \cdot W^{0.425} \cdot H^{0.725}$

where:

• $W$ = weight (kg)

• $H$ = height (cm)

Value

the body surface area (m^2)

See Also

Other body_composition: aibw(), bmi(), bmic(), bsa(), ibw(), mosteller_bsa()

Examples

b <- dubois_bsa(80.56, 167)

df <- data.frame(
"WT" = c(80.56, 71.53, 81.04, 70.17),
"HT" = c(167, 161, 163, 164)
)
df <- dplyr::mutate(df, bsa = dubois_bsa(WT, HT))
df

Description

Calculate Estimated Glomerular Filtration Rate

Usage

egfr(sexf, raceb, age, creat, cystc, height, method = "CKDEPI 2021")

Arguments

Value

the eGFR calculated based on method.

See Also

Other renal_function: aegfr(), ckdepi_2009_egfr(), ckdepi_2021_egfr(), ckdepi_2021_egfr_cystatin(), crcl(), mdrd_egfr(), rfc(), schwartz_egfr()

Examples

e <- egfr(TRUE, TRUE, 24, 1, "CKDEPI 2009")

df <- data.frame(
  "SEXF" = c(TRUE, FALSE, TRUE, FALSE),
  "RACEB" = c(FALSE, FALSE, TRUE, FALSE),
  "AGE" = c(24, 24, 23, 24),
  "CREAT" = c(1, 1, 2, 1)
)
df <- dplyr::mutate(df, egfr = egfr(SEXF, RACEB, AGE, CREAT, "CKDEPI 2009"))
df

Description

Convert Ethnicity to Numeric Code

Usage

ethnicn(ethnicc)

Arguments

Value

the standard yspec numeric value for the inputted Ethnic character

See Also

Other demographics: is_asian(), is_black(), is_female(), is_hispanic_or_latino(), is_not_hispanic_or_latino(), is_other(), is_white(), racen(), sexf()

Examples

ethnicn("HISPANIC OR LATINO") # 1

ethnicn("NOT HISPANIC OR LATINO") # 0

ethnicn("UNKNOWN") # -999

Description

Calculate Geometric Coefficient of Variation

Usage

geom_cv(x, na.rm = FALSE)

Arguments

Details

The geometric coefficient of variation is calculated as:

$GCV = \sqrt{\exp(Var[\log(x)]) - 1}$

Value

the geometric CV of the input vector x

See Also

Other statistics: cor_df(), cv(), geom_mean(), geom_sd()

Examples

geom_cv(c(1, 2, 3, 2, 1))

Description

Calculate Geometric Mean

Usage

geom_mean(x, na.rm = FALSE)

Arguments

Details

The geometric mean is calculated as:

$GM = \exp\left(\mathbb{E}[\log(x)]\right)$

Value

geometric mean of input vector x

See Also

Other statistics: cor_df(), cv(), geom_cv(), geom_sd()

Examples

geom_mean(c(1, 2, 3, 2, 1))

Description

Calculate Geometric Standard Deviation

Usage

geom_sd(x, na.rm = FALSE)

Arguments

Details

The geometric standard deviation is calculated as:

$GSD = \exp\left(\sqrt{Var[\log(x)]}\right)$

Value

the geometric standard deviation of x

See Also

Other statistics: cor_df(), cv(), geom_cv(), geom_mean()

Examples

geom_sd(c(1, 2, 3, 2, 1))

Description

Get Unique Parameter-Unit Combinations

Usage

get_unique_units_df(params, units)

Arguments

Value

a dataframe with distinct units and parameters with IU replaced to U and mu replaced with u

See Also

Other unit_checking: check_for_unique_units()

Examples

df <- data.frame(
  PARAM = c(
    "ALB","ALT","AST","CR","TBIL",
    "ALB","CR","TBIL","ALT","AST"),
  UNIT = c(
    "g/L","U/L","U/L","umol/L","umol/L",
    "U/L","μmol/L","μmol/L","IU/L","IU/L")
)
get_unique_units_df(df$PARAM, df$UNIT)

Description

This function categorizes hepatic function impairment using the National Cancer Institute Organ Dysfunction Working Group (NCI-ODWG) criteria. It evaluates aspartate aminotransferase (AST) and bilirubin levels relative to their upper limits of normal to determine hepatic impairment severity. The function handles edge cases where bilirubin values are very close to category boundaries using floating-point tolerant comparisons.

Usage

hfc(ast, ulnast, bili, ulnbili)

Arguments

Details

The NCI-ODWG hepatic function categories are defined as:

• 1: Normal: AST ≤ ULN AND bilirubin ≤ ULN

• 2: Mild impairment: AST > ULN OR bilirubin > ULN but ≤ 1.5 × ULN

• 3: Moderate impairment: Bilirubin > 1.5 × ULN but ≤ 3 × ULN

• 4: Severe impairment: Bilirubin > 3 × ULN

Special handling: The function uses dplyr::near() for boundary comparisons when bilirubin values are very close to 1.5 × ULN or 3 × ULN to handle floating-point precision issues that can occur with calculated thresholds.

Value

Integer vector of hepatic function categories (1-4). Returns -999 for missing values.

References

National Cancer Institute Organ Dysfunction Working Group criteria for hepatic impairment

Examples

# Single patient with normal hepatic function
hfc(ast = 15, ulnast = 33, bili = 0.6, ulnbili = 1.2)

# Multiple patients with different impairment levels
hfc(ast = c(25, 45, 30, 20),
     ulnast = c(33, 33, 33, 33),
     bili = c(0.8, 1.0, 2.5, 4.0),
     ulnbili = c(1.2, 1.2, 1.2, 1.2))

# Edge case: bilirubin exactly at boundary
hfc(ast = 25, ulnast = 33, bili = 1.8, ulnbili = 1.2)  # 1.8 = 1.5 * 1.2

# Pipeline example with realistic data
library(dplyr)

patients <- data.frame(
  ID = 1:6,
  AST = c(15, 45, 28, 35, 22, 30),
  ULNAST = 33,
  BILI = c(0.8, 1.0, 2.2, 4.5, 1.8, 0.9),
  ULNBILI = 1.2
)

patients %>%
  mutate(BHFC = hfc(AST, ULNAST, BILI, ULNBILI))

Description

Calculates ideal body weight using the Devine equation. By default, applies intercepts for individuals shorter than 5 feet (152.4 cm).

Usage

ibw(height, sexf, age, allow_ibw_lt_intercept = TRUE)

Arguments

Details

The Devine equation for ideal body weight:

$IBW = I + 2.3 \cdot (H_{in} - 60)$

where:

• $I$ = 50 kg (male) or 45.5 kg (female)

• $H_{in}$ = height in inches (60 inches = 5 feet = 152.4 cm)

Value

ideal body weight (kg)

References

The Origin of the "Ideal" Body Weight Equations, by Pai and Paloucek. The annals of Pharmacotherapy 2000 september, volumn 34

See Also

Other body_composition: aibw(), bmi(), bmic(), bsa(), dubois_bsa(), mosteller_bsa()

Examples

df <- data.frame(
  ID = 1:6,
  HEIGHT = c(160, 170, 175, 165, 180, 150),
  SEX = c(1, 0, 0, 1, 0, 1),
 AGE = c(18, 27, 34, 33, 29, 30)
)
df <- dplyr::mutate(df, IBW = ibw(HEIGHT, SEX, AGE))
df

Description

Check if Race is Asian

Usage

is_asian(x)

Arguments

Value

boolean representing Race == Asian

See Also

Other demographics: ethnicn(), is_black(), is_female(), is_hispanic_or_latino(), is_not_hispanic_or_latino(), is_other(), is_white(), racen(), sexf()

Examples

is_asian("ASIAN")

is_asian("BLACK")

is_asian(3)

Description

Check if Race is Black

Usage

is_black(x)

Arguments

Value

boolean representing Race == Black

See Also

Other demographics: ethnicn(), is_asian(), is_female(), is_hispanic_or_latino(), is_not_hispanic_or_latino(), is_other(), is_white(), racen(), sexf()

Examples

is_black("WHITE")

is_black(c("AFRICAN AMERICAN", "BLACK"))

is_black(2)

Description

Check if Sex is Female

Usage

is_female(x)

Arguments

Value

boolean representing female

See Also

Other demographics: ethnicn(), is_asian(), is_black(), is_hispanic_or_latino(), is_not_hispanic_or_latino(), is_other(), is_white(), racen(), sexf()

Examples

is_female("F")

is_female(c("MALE", "FEMALE"))

is_female(c(1, 0, -999))

Description

Check if Ethnicity is Hispanic or Latino

Usage

is_hispanic_or_latino(x)

Arguments

Value

boolean representing Ethnic == "Hispanic or Latino"

See Also

Other demographics: ethnicn(), is_asian(), is_black(), is_female(), is_not_hispanic_or_latino(), is_other(), is_white(), racen(), sexf()

Examples

is_hispanic_or_latino("HISPANIC OR LATINO")

is_hispanic_or_latino("NOT HISPANIC OR LATINO")

is_hispanic_or_latino("UNKNOWN")

is_hispanic_or_latino(1)

Description

Check if Ethnicity is Not Hispanic or Latino

Usage

is_not_hispanic_or_latino(x)

Arguments

Value

boolean representing Ethnic == "Not Hispanic or Latino"

See Also

Other demographics: ethnicn(), is_asian(), is_black(), is_female(), is_hispanic_or_latino(), is_other(), is_white(), racen(), sexf()

Examples

is_not_hispanic_or_latino("HISPANIC OR LATINO")

is_not_hispanic_or_latino("NOT HISPANIC OR LATINO")

is_not_hispanic_or_latino("UNKNOWN")

is_not_hispanic_or_latino(0)

Description

Check if Race is Other

Usage

is_other(x)

Arguments

Value

boolean representing Race == Other

See Also

Other demographics: ethnicn(), is_asian(), is_black(), is_female(), is_hispanic_or_latino(), is_not_hispanic_or_latino(), is_white(), racen(), sexf()

Examples

is_other("OTHER")

is_other("BLACK")

is_other(4)

Description

Check if Race is White

Usage

is_white(x)

Arguments

Value

boolean representing Race == White

See Also

Other demographics: ethnicn(), is_asian(), is_black(), is_female(), is_hispanic_or_latino(), is_not_hispanic_or_latino(), is_other(), racen(), sexf()

Examples

is_white("WHITE")

is_white("BLACK")

is_white(1)

Description

Calculate eGFR Using MDRD Equation

Usage

mdrd_egfr(sexf, raceb, age, creat)

Arguments

Details

The MDRD equation:

$eGFR = 175 \cdot S_{cr}^{-1.154} \cdot A^{-0.203} \cdot 0.742^F \cdot 1.212^B$

where:

• $S_{cr}$ = serum creatinine (mg/dL)

• $A$ = age (years)

• $F$ = 1 (female) or 0 (male)

• $B$ = 1 (Black) or 0 (other)

Value

the eGFR in mL/min/1.73 m^2

See Also

Other renal_function: aegfr(), ckdepi_2009_egfr(), ckdepi_2021_egfr(), ckdepi_2021_egfr_cystatin(), crcl(), egfr(), rfc(), schwartz_egfr()

Examples

e <- mdrd_egfr(TRUE, TRUE, 24, 1)

df <- data.frame(
  "SEXF" = c(TRUE, FALSE, TRUE, FALSE),
  "RACEB" = c(FALSE, FALSE, TRUE, FALSE),
  "AGE" = c(24, 24, 23, 24),
  "CREAT" = c(1, 1, 2, 1)
)
df <- dplyr::mutate(df, egfr = mdrd_egfr(SEXF, RACEB, AGE, CREAT))
df

Description

Calculate Body Surface Area Using Mosteller Equation

Usage

mosteller_bsa(weight, height)

Arguments

Details

The Mosteller equation for BSA:

$BSA = \sqrt{\frac{W \cdot H}{3600}}$

where:

• $W$ = weight (kg)

• $H$ = height (cm)

Value

the body surface area (m^2)

See Also

Other body_composition: aibw(), bmi(), bmic(), bsa(), dubois_bsa(), ibw()

Examples

mosteller_bsa(70, 170)

Description

Convert Race to Numeric Code

Usage

racen(racec)

Arguments

Value

the standard yspec numeric value for the inputted Race character

See Also

Other demographics: ethnicn(), is_asian(), is_black(), is_female(), is_hispanic_or_latino(), is_not_hispanic_or_latino(), is_other(), is_white(), sexf()

Examples

racen("WHITE") # 1

racen("BLACK") # 2

racen("ASIAN") # 3

racen("OTHER") # 4

racen("UNKNOWN") # -999

Description

Read CSV File with Hash Verification

Usage

read_csv_with_hash(csv_file_path, ..., algo = "blake3")

Arguments

Value

dataframe of data within file

See Also

Other file_io: read_excel_with_hash(), read_file_with_hash(), read_hashed_file(), read_parquet_with_hash(), read_pzfx_with_hash(), read_sas_with_hash(), read_xpt_with_hash(), write_csv_with_hash(), write_file_with_hash(), write_parquet_with_hash()

Examples

## Not run: 
read_csv_with_hash("data/derived/example_data.csv")

## End(Not run)

Description

Read Excel File with Hash Verification

Usage

read_excel_with_hash(xlsx_file_path, ..., algo = "blake3")

Arguments

Value

a dataframe(?) of data within file

See Also

Other file_io: read_csv_with_hash(), read_file_with_hash(), read_hashed_file(), read_parquet_with_hash(), read_pzfx_with_hash(), read_sas_with_hash(), read_xpt_with_hash(), write_csv_with_hash(), write_file_with_hash(), write_parquet_with_hash()

Examples

## Not run: 
read_excel_with_hash("data/source/example.xpt")

## End(Not run)

Description

Read Data File with Hash Verification

Usage

read_file_with_hash(file_path, ..., algo = "blake3")

Arguments

Value

data within the supplied file

See Also

Other file_io: read_csv_with_hash(), read_excel_with_hash(), read_hashed_file(), read_parquet_with_hash(), read_pzfx_with_hash(), read_sas_with_hash(), read_xpt_with_hash(), write_csv_with_hash(), write_file_with_hash(), write_parquet_with_hash()

Examples

## Not run: 
dat <- read_file_with_hash("data/derived/PK_data.parquet")
dat2 <- read_file_with_hash("data/source/data.csv")

## End(Not run)

Description

Read File with Required Hash Match

Usage

read_hashed_file(file_path, hash, ..., algo = "blake3")

Arguments

Value

data object of contents of file_path

See Also

Other file_io: read_csv_with_hash(), read_excel_with_hash(), read_file_with_hash(), read_parquet_with_hash(), read_pzfx_with_hash(), read_sas_with_hash(), read_xpt_with_hash(), write_csv_with_hash(), write_file_with_hash(), write_parquet_with_hash()

Examples

## Not run: 
file_path <- "data/derived/example_pk.parquet"

hash <- 0cfd6da55e6c1e198effe1e584c26d79
read_hashed_file(file_path, hash)

## End(Not run)

Description

Read Parquet File with Hash Verification

Usage

read_parquet_with_hash(parquet_file_path, ..., algo = "blake3")

Arguments

Value

a tibble of data within file

See Also

Other file_io: read_csv_with_hash(), read_excel_with_hash(), read_file_with_hash(), read_hashed_file(), read_pzfx_with_hash(), read_sas_with_hash(), read_xpt_with_hash(), write_csv_with_hash(), write_file_with_hash(), write_parquet_with_hash()

Examples

## Not run: 
read_parquet_with_hash("data/derived/example_data.parquet")

## End(Not run)

Description

Read Prism PZFX File with Hash Verification

Usage

read_pzfx_with_hash(pzfx_file_path, ..., algo = "blake3")

Arguments

Value

data within the table of the pzfx file

See Also

Other file_io: read_csv_with_hash(), read_excel_with_hash(), read_file_with_hash(), read_hashed_file(), read_parquet_with_hash(), read_sas_with_hash(), read_xpt_with_hash(), write_csv_with_hash(), write_file_with_hash(), write_parquet_with_hash()

Examples

## Not run: 
read_pzfx_with_hash("mydata.pzfx", table = "experiment1")

## End(Not run)

Description

Read SAS File with Hash Verification

Usage

read_sas_with_hash(sas_file_path, ..., algo = "blake3")

Arguments

Value

a dataframe(?) of data within file

See Also

Other file_io: read_csv_with_hash(), read_excel_with_hash(), read_file_with_hash(), read_hashed_file(), read_parquet_with_hash(), read_pzfx_with_hash(), read_xpt_with_hash(), write_csv_with_hash(), write_file_with_hash(), write_parquet_with_hash()

Examples

## Not run: 
read_sas_with_hash("data/source/example.sas7bdat")

## End(Not run)

Description

Read XPT File with Hash Verification

Usage

read_xpt_with_hash(xpt_file_path, ..., algo = "blake3")

Arguments

Value

a dataframe(?) of data within file

See Also

Other file_io: read_csv_with_hash(), read_excel_with_hash(), read_file_with_hash(), read_hashed_file(), read_parquet_with_hash(), read_pzfx_with_hash(), read_sas_with_hash(), write_csv_with_hash(), write_file_with_hash(), write_parquet_with_hash()

Examples

## Not run: 
read_xpt_with_hash("data/source/example.xpt")

## End(Not run)

Description

This function categorizes renal function based on estimated glomerular filtration rate (eGFR), creatinine clearance, or other renal function estimators. It supports both clinical and regulatory categorization standards and can convert between absolute (mL/min) and relative (mL/min/1.73m²) units using body surface area.

Usage

rfc(
  estimator = NULL,
  bsa = NULL,
  category_standard = c("regulatory", "clinical"),
  absolute_units = NULL
)

Arguments

Details

The function applies different categorization schemes based on the category_standard:

Regulatory categories (uses mL/min):

• 1: Normal: ≥90 mL/min

• 2: Mild impairment: 60-89 mL/min

• 3: Moderate impairment: 30-59 mL/min

• 4: Severe impairment: <30 mL/min

Clinical categories (uses mL/min/1.73m²):

• 1: Normal: ≥90 mL/min/1.73m²

• 2: Mild impairment: 60-89 mL/min/1.73m²

• 3: Moderate impairment: 30-59 mL/min/1.73m²

• 4: Severe impairment: 15-29 mL/min/1.73m²

• 5: End-stage: <15 mL/min/1.73m²

When unit conversion is required, the function uses:

• Absolute to relative: relative = 1.73 (absolute / bsa)

• Relative to absolute: absolute = relative (bsa / 1.73)

Value

Integer vector of renal impairment categories (1-4 for regulatory, 1-5 for clinical). Returns -999 for missing values. Includes a category_standard attribute indicating the source ("FDA" or "KDIGO").

References

FDA Guidance for Industry: Pharmacokinetics in Patients with Impaired Renal Function. https://www.fda.gov/media/78573/download

KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. https://www.kidney-international.org/action/showPdf?pii=S0085-2538(23)00766-4

See Also

egfr for calculating eGFR, crcl for creatinine clearance, bsa for body surface area calculation

Other renal_function: aegfr(), ckdepi_2009_egfr(), ckdepi_2021_egfr(), ckdepi_2021_egfr_cystatin(), crcl(), egfr(), mdrd_egfr(), schwartz_egfr()

Examples

# Regulatory categories with absolute units (creatinine clearance)
rfc(estimator = c(95, 75, 45, 25), absolute_units = TRUE)

# Clinical categories with relative units (eGFR)
rfc(
  estimator = c(95, 75, 45, 25, 10),
  absolute_units = FALSE,
  category_standard = "clinical"
)

# Convert relative eGFR to regulatory categories
rfc(
  estimator = 65,
  absolute_units = FALSE,
  bsa = 1.8
)

# Pipeline example with realistic data
df <- data.frame(
  ID = 1:4,
  SEX = c("F", "M", "F", "M"),
  AGE = c(65, 45, 70, 50),
  CREAT = c(1.2, 0.9, 1.5, 1.1),
  WEIGHT = c(70, 80, 60, 85),
  HEIGHT = c(165, 175, 160, 180),
  RACE = c("WHITE", "BLACK", "OTHER", "ASIAN")
)

library(dplyr)
df %>%
  mutate(
    BSA = bsa(WEIGHT, HEIGHT, method = "Dubois"),
    EGFR = egfr(is_female(SEX), is_black(RACE), AGE, CREAT),
    AEGFR = aegfr(EGFR, BSA),
    # Clinical categories using relative eGFR directly
    BRFC_CLINICAL = rfc(EGFR, category_standard = "clinical"),
    # Regulatory categories - convert relative eGFR to absolute
    BRFC_REGULATORY_REL = rfc(EGFR, BSA),
    # Regulatory categories - AEGFR already absolute
    BRFC_REGULATORY_ABS = rfc(AEGFR)
  )
df

Description

Priority:

1. If sdig is provided, round x to that many significant digits.

2. If sdig is NULL but ref is provided, infer the number of significant digits from ref.

3. If both are NULL, return x unchanged.

Usage

round_like(x, ref = NULL, sdig = NULL)

Arguments

Value

A numeric vector rounded to the appropriate precision.

Examples

round_like(123.456, sdig = 2)       # -> 120
round_like(123.456, ref = 12.3)     # -> 123
round_like(123.456, ref = 12.34)    # -> 123.5
round_like(123.456)                 # -> 123.456

Description

Calculate eGFR Using Schwartz Equation

Usage

schwartz_egfr(height, creat)

Arguments

Details

The Schwartz equation for pediatric eGFR:

$eGFR = 0.413 \cdot \frac{H}{S_{cr}}$

where:

• $H$ = height (cm)

• $S_{cr}$ = serum creatinine (mg/dL)

Value

eGFR in mL/min/1.73m^2

See Also

Other renal_function: aegfr(), ckdepi_2009_egfr(), ckdepi_2021_egfr(), ckdepi_2021_egfr_cystatin(), crcl(), egfr(), mdrd_egfr(), rfc()

Examples

schwartz_egfr(100, 1)

Description

Also returns numeric for single character Sex characters "F" and "M"

Usage

sexf(sex)

Arguments

Value

the standard yspec numeric value for the inputted Sex character

See Also

Other demographics: ethnicn(), is_asian(), is_black(), is_female(), is_hispanic_or_latino(), is_not_hispanic_or_latino(), is_other(), is_white(), racen()

Examples

sexf("FEMALE") # 1
sexf("female") # 1
sexf("f") # 1

sexf("MALE") # 0

sexf("NOT SPECIFIED") # 0

Description

Write CSV File with Hash Output

Usage

write_csv_with_hash(data, csv_path, ...)

Arguments

Value

Nothing, creates csv_path file and prints hash of the file

See Also

Other file_io: read_csv_with_hash(), read_excel_with_hash(), read_file_with_hash(), read_hashed_file(), read_parquet_with_hash(), read_pzfx_with_hash(), read_sas_with_hash(), read_xpt_with_hash(), write_file_with_hash(), write_parquet_with_hash()

Examples

## Not run: 
df <- data.frame(
  "a" = c(1, 2, 3, 4)
  "b" = c("A", "B", "C", "D")
)
write_csv_with_hash(df, "test/test.csv")

## End(Not run)

Description

Write Data File with Hash Output

Usage

write_file_with_hash(data, path, overwrite = FALSE, ...)

Arguments

Value

Nothing, File is created and hash of created file is printed

See Also

Other file_io: read_csv_with_hash(), read_excel_with_hash(), read_file_with_hash(), read_hashed_file(), read_parquet_with_hash(), read_pzfx_with_hash(), read_sas_with_hash(), read_xpt_with_hash(), write_csv_with_hash(), write_parquet_with_hash()

Examples

## Not run: 
df <- data.frame(
  "a" = c(1, 2, 3, 4)
  "b" = c("A", "B", "C", "D")
)
write_data_with_hash(df, "data.csv")

## End(Not run)

Description

Write Parquet File with Hash Output

Usage

write_parquet_with_hash(data, parquet_path, ...)

Arguments

Value

Nothing. creates parquet_path file and prints hash

See Also

Other file_io: read_csv_with_hash(), read_excel_with_hash(), read_file_with_hash(), read_hashed_file(), read_parquet_with_hash(), read_pzfx_with_hash(), read_sas_with_hash(), read_xpt_with_hash(), write_csv_with_hash(), write_file_with_hash()

Examples

## Not run: 
df <- data.frame(
  "a" = c(1, 2, 3, 4)
  "b" = c("A", "B", "C", "D")
)
write_parquet_with_hash(df, "test/test.parquet")

## End(Not run)