Calculations

library(dmcognigen)
library(dplyr)

The calculations family is a collection of functions intended to facilitate the calculation of variables that mostly use standard equations (e.g: CrCl, eGFR, BMI etc). This vignette demonstrates:

How these functions work when required variables are missing.
Description of calculations functions.
Examples use-cases.

In their simplest form, these functions apply and report formulas.

calculate_crcl(
  age = 50,
  scr = 1,
  sexf = 0,
  wtkg = 70
)
#> Formula to calculate CRCL: 
#>   Males: CRCL [mL/min] = ((140 - AGE [y]) × WTKG [kg]) ÷ (72 × SCR [mg/dL])
#>   Females: CRCL [mL/min] = (((140 - AGE [y]) × WTKG [kg]) ÷ (72 × SCR [mg/dL])) × 0.85
#> [1] 87.5

Detect missing variables from environment

When expected variables are missing, an attempt is made to detect them in the environment.

The calculation function checks the calling environment for missing variables with a case-insensitive match.
An error is raised if either no match is found or more than one match is found.
If a single match is found, it is used and the user is notified.

Source datasets

For examples and testing, there are calculation-ready sample datasets incorporated into the package.

`dmcognigen_cov`

The dmcognigen_cov dataset is a data.frame with demographic/covariate information. It contains the variables required for most calculation functions.

glimpse(dmcognigen_cov)
#> Rows: 254
#> Columns: 53
#> $ DOMAIN   <chr> "DM", "DM", "DM", "DM", "DM", "DM", "DM", "DM", "DM", "DM", "…
#> $ STUDYID  <chr> "CDISCPILOT01", "CDISCPILOT01", "CDISCPILOT01", "CDISCPILOT01…
#> $ USUBJID  <chr> "01-701-1015", "01-701-1023", "01-701-1028", "01-701-1033", "…
#> $ ID       <dbl> 10101, 10102, 10103, 10104, 10105, 10106, 10108, 10109, 10110…
#> $ RACEN    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1…
#> $ RACEC    <chr> "White/Caucasian", "White/Caucasian", "White/Caucasian", "Whi…
#> $ SEXF     <dbl> 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1…
#> $ SEXFC    <chr> "Female", "Male", "Male", "Male", "Female", "Female", "Male",…
#> $ HTCM     <dbl> 147.32, 162.56, 177.80, 175.26, 154.94, 148.59, 168.91, 158.2…
#> $ WTKG     <dbl> 54.43, 80.29, 99.34, 88.45, 62.60, 67.13, 78.02, 59.88, 78.93…
#> $ AST      <dbl> 40, 21, 24, 20, 23, 25, 19, 28, 26, 15, 24, 27, 14, 25, 24, 2…
#> $ ASTULN   <dbl> 34, 36, 36, 36, 34, 34, 36, 34, 36, 36, 36, 34, 34, 36, 34, 3…
#> $ SCR      <dbl> 0.9, 1.4, 1.4, 1.5, 1.0, 1.0, 1.4, 0.9, 1.3, 1.0, 1.4, 1.1, 0…
#> $ SCRULN   <dbl> 1.4, 1.6, 1.6, 1.6, 1.4, 1.4, 1.6, 1.4, 1.6, 1.6, 1.6, 1.4, 1…
#> $ TBIL     <dbl> 0.6, 0.7, 1.1, 0.8, 0.6, 0.4, 0.4, 0.4, 0.5, 0.7, 0.5, 0.9, 0…
#> $ TBILULN  <dbl> 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1…
#> $ ASTCAT   <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ BMI      <dbl> 25.07927, 30.38324, 31.42394, 28.79600, 26.07638, 30.40447, 2…
#> $ BSA      <dbl> 1.514484, 1.928392, 2.234674, 2.092314, 1.662531, 1.693187, 1…
#> $ IBW      <dbl> 45.37000, 59.05000, 70.15000, 68.30000, 50.32000, 46.19500, 6…
#> $ CRCL     <dbl> 54.97598, 60.53611, 68.00060, 54.05278, 46.55875, 43.58788, 5…
#> $ CRCLP    <dbl> 54.97598, 44.52183, 48.01935, 41.73889, 37.42550, 29.99467, 4…
#> $ EGFR     <dbl> 63.23827, 51.02133, 49.95752, 45.74827, 53.76310, 52.69506, 5…
#> $ EGFRSCHW <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ IBWCHILD <dbl> 35.81025, 43.60249, 52.16119, 50.68151, 39.61057, 36.43033, 4…
#> $ LBM      <dbl> 38.03714, 57.09378, 69.31683, 64.69332, 42.82276, 41.62153, 5…
#> $ TBILCAT  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ RFCAT    <dbl> 2, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 3, 3, NA, 2, 2, …
#> $ RFCATC   <chr> "Mild Impairment (60-89 mL/min)", "Moderate Impairment (30-59…
#> $ NCILIV   <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ NCILIVC  <chr> "Mild Group B1", "Normal Group A", "Normal Group A", "Normal …
#> $ SUBJID   <chr> "1015", "1023", "1028", "1033", "1034", "1047", "1097", "1111…
#> $ RFSTDTC  <chr> "2014-01-02", "2012-08-05", "2013-07-19", "2014-03-18", "2014…
#> $ RFENDTC  <chr> "2014-07-02", "2012-09-02", "2014-01-14", "2014-04-14", "2014…
#> $ RFXSTDTC <chr> "2014-01-02", "2012-08-05", "2013-07-19", "2014-03-18", "2014…
#> $ RFXENDTC <chr> "2014-07-02", "2012-09-01", "2014-01-14", "2014-03-31", "2014…
#> $ RFICDTC  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ RFPENDTC <chr> "2014-07-02T11:45", "2013-02-18", "2014-01-14T11:10", "2014-0…
#> $ DTHDTC   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ DTHFL    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SITEID   <chr> "701", "701", "701", "701", "701", "701", "701", "701", "701"…
#> $ AGE      <dbl> 63, 64, 71, 74, 77, 85, 68, 81, 84, 52, 84, 81, 75, 57, 79, 5…
#> $ AGEU     <chr> "YEARS", "YEARS", "YEARS", "YEARS", "YEARS", "YEARS", "YEARS"…
#> $ SEX      <chr> "F", "M", "M", "M", "F", "F", "M", "F", "M", "M", "M", "F", "…
#> $ RACE     <chr> "WHITE", "WHITE", "WHITE", "WHITE", "WHITE", "WHITE", "WHITE"…
#> $ ETHNIC   <chr> "HISPANIC OR LATINO", "HISPANIC OR LATINO", "NOT HISPANIC OR …
#> $ ARMCD    <chr> "Pbo", "Pbo", "Xan_Hi", "Xan_Lo", "Xan_Hi", "Pbo", "Xan_Lo", …
#> $ ARM      <chr> "Placebo", "Placebo", "Xanomeline High Dose", "Xanomeline Low…
#> $ ACTARMCD <chr> "Pbo", "Pbo", "Xan_Hi", "Xan_Lo", "Xan_Hi", "Pbo", "Xan_Lo", …
#> $ ACTARM   <chr> "Placebo", "Placebo", "Xanomeline High Dose", "Xanomeline Low…
#> $ COUNTRY  <chr> "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA"…
#> $ DMDTC    <chr> "2013-12-26", "2012-07-22", "2013-07-11", "2014-03-10", "2014…
#> $ DMDY     <dbl> -7, -14, -8, -8, -7, -21, -9, -13, -7, -13, -6, -5, -13, -9, …

`dmcognigen_conc`

The dmcognigen_conc dataset is a data.frame with concentration variables.

glimpse(dmcognigen_conc)
#> Rows: 3,556
#> Columns: 30
#> $ DOMAIN   <chr> "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "PC", "…
#> $ STUDYID  <chr> "CDISCPILOT01", "CDISCPILOT01", "CDISCPILOT01", "CDISCPILOT01…
#> $ USUBJID  <chr> "01-701-1015", "01-701-1015", "01-701-1015", "01-701-1015", "…
#> $ DVID     <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ DVIDC    <chr> "Xanomeline Concentration (ug/mL)", "Xanomeline Concentration…
#> $ EVID     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ MDV      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ DV       <dbl> 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005…
#> $ LOGDV    <dbl> -5.298317, -5.298317, -5.298317, -5.298317, -5.298317, -5.298…
#> $ BLQFN    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ LLOQ     <dbl> 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0…
#> $ DTTM     <dttm> 2014-01-01 23:30:00, 2014-01-02 00:05:00, 2014-01-02 00:30:0…
#> $ DAY      <dbl> -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, -1, 1, 1, 1, 1, 1,…
#> $ PCSEQ    <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1, 2, 3, 4, 5,…
#> $ PCTESTCD <chr> "XAN", "XAN", "XAN", "XAN", "XAN", "XAN", "XAN", "XAN", "XAN"…
#> $ PCTEST   <chr> "XANOMELINE", "XANOMELINE", "XANOMELINE", "XANOMELINE", "XANO…
#> $ PCORRES  <chr> "<BLQ", "<BLQ", "<BLQ", "<BLQ", "<BLQ", "<BLQ", "<BLQ", "<BLQ…
#> $ PCORRESU <chr> "ug/ml", "ug/ml", "ug/ml", "ug/ml", "ug/ml", "ug/ml", "ug/ml"…
#> $ PCSTRESC <chr> "<BLQ", "<BLQ", "<BLQ", "<BLQ", "<BLQ", "<BLQ", "<BLQ", "<BLQ…
#> $ PCSTRESN <dbl> 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA,…
#> $ PCSTRESU <chr> "ug/ml", "ug/ml", "ug/ml", "ug/ml", "ug/ml", "ug/ml", "ug/ml"…
#> $ PCNAM    <chr> "Imaginary Labs", "Imaginary Labs", "Imaginary Labs", "Imagin…
#> $ PCSPEC   <chr> "PLASMA", "PLASMA", "PLASMA", "PLASMA", "PLASMA", "PLASMA", "…
#> $ PCLLOQ   <dbl> 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0…
#> $ VISIT    <chr> "BASELINE", "BASELINE", "BASELINE", "BASELINE", "BASELINE", "…
#> $ VISITNUM <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3…
#> $ PCDTC    <chr> "2014-01-01T23:30:00", "2014-01-02T00:05:00", "2014-01-02T00:…
#> $ PCDY     <dbl> -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, -1, 1, 1, 1, 1, 1,…
#> $ PCTPT    <chr> "Pre-dose", "5 Min Post-dose", "30 Min Post-dose", "1h Post-d…
#> $ PCTPTNUM <dbl> -0.50, 0.08, 0.50, 1.00, 1.50, 2.00, 4.00, 6.00, 8.00, 12.00,…

Functions

Each function performs calculations according to some formula, so the same function can be used to calculate both baseline and time-varying versions of a variable. For instance, calculate_bmi can calculate both BMI (baseline) and BMIT (time-varying). If you provide time varying WTKG and HTCM, then the result will be time-varying. Each function prints the formula that was used in a readable format.

`calculate_crcl()`

Calculate Creatinine Clearance (CrCl), a measure of renal function based on age, weight, gender, and serum creatinine. This is the Cockcroft and Gault Formula.

Males: CRCL [mL/min] = ((140 - AGE [y]) × WTKG [kg]) ÷ (72 × SCR [mg/dL])
Females: CRCL [mL/min] = (((140 - AGE [y]) × WTKG [kg]) ÷ (72 × SCR [mg/dL])) × 0.85

This function returns a numeric vector the same length as its inputs. Following are the inputs:

age: Age (y). Numeric vector.
wtkg: Weight (kg). Numeric vector.
scr: Serum Creatinine (mg/dL). Numeric vector.
sexf: Sex. Numeric vector including values of 0 and/or 1. 0=Male; 1=Female.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    CRCL = calculate_crcl(
      age = AGE,
      scr = SCR,
      sexf = SEXF,
      wtkg = WTKG
    )
  ) %>% 
  select(USUBJID, AGE, SCR, SEXF, WTKG, CRCL)
#> Formula to calculate CRCL: 
#>   Males: CRCL [mL/min] = ((140 - AGE [y]) × WTKG [kg]) ÷ (72 × SCR [mg/dL])
#>   Females: CRCL [mL/min] = (((140 - AGE [y]) × WTKG [kg]) ÷ (72 × SCR [mg/dL])) × 0.85
#> # A tibble: 254 × 6
#>    USUBJID       AGE   SCR  SEXF  WTKG  CRCL
#>    <chr>       <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 01-701-1015    63   0.9     1  54.4  55.0
#>  2 01-701-1023    64   1.4     0  80.3  60.5
#>  3 01-701-1028    71   1.4     0  99.3  68.0
#>  4 01-701-1033    74   1.5     0  88.4  54.1
#>  5 01-701-1034    77   1       1  62.6  46.6
#>  6 01-701-1047    85   1       1  67.1  43.6
#>  7 01-701-1097    68   1.4     0  78.0  55.7
#>  8 01-701-1111    81   0.9     1  59.9  46.3
#>  9 01-701-1115    84   1.3     0  78.9  47.2
#> 10 01-701-1118    52   1       0  71.2  87.0
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    CRCL = calculate_crcl()
  ) %>% 
  select(USUBJID, AGE, SCR, SEXF, WTKG, CRCL)
#> AGE variable found and used for the age argument.
#> SCR variable found and used for the scr argument.
#> SEXF variable found and used for the sexf argument.
#> WTKG variable found and used for the wtkg argument.
#> Formula to calculate CRCL: 
#>   Males: CRCL [mL/min] = ((140 - AGE [y]) × WTKG [kg]) ÷ (72 × SCR [mg/dL])
#>   Females: CRCL [mL/min] = (((140 - AGE [y]) × WTKG [kg]) ÷ (72 × SCR [mg/dL])) × 0.85
#> # A tibble: 254 × 6
#>    USUBJID       AGE   SCR  SEXF  WTKG  CRCL
#>    <chr>       <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 01-701-1015    63   0.9     1  54.4  55.0
#>  2 01-701-1023    64   1.4     0  80.3  60.5
#>  3 01-701-1028    71   1.4     0  99.3  68.0
#>  4 01-701-1033    74   1.5     0  88.4  54.1
#>  5 01-701-1034    77   1       1  62.6  46.6
#>  6 01-701-1047    85   1       1  67.1  43.6
#>  7 01-701-1097    68   1.4     0  78.0  55.7
#>  8 01-701-1111    81   0.9     1  59.9  46.3
#>  9 01-701-1115    84   1.3     0  78.9  47.2
#> 10 01-701-1118    52   1       0  71.2  87.0
#> # ℹ 244 more rows

dmcognigen_cov %>% 
  mutate(
    CRCL = calculate_crcl(age = AGE)
  ) %>% 
  select(USUBJID, AGE, SCR, SEXF, WTKG, CRCL)
#> SCR variable found and used for the scr argument.
#> SEXF variable found and used for the sexf argument.
#> WTKG variable found and used for the wtkg argument.
#> Formula to calculate CRCL: 
#>   Males: CRCL [mL/min] = ((140 - AGE [y]) × WTKG [kg]) ÷ (72 × SCR [mg/dL])
#>   Females: CRCL [mL/min] = (((140 - AGE [y]) × WTKG [kg]) ÷ (72 × SCR [mg/dL])) × 0.85
#> # A tibble: 254 × 6
#>    USUBJID       AGE   SCR  SEXF  WTKG  CRCL
#>    <chr>       <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 01-701-1015    63   0.9     1  54.4  55.0
#>  2 01-701-1023    64   1.4     0  80.3  60.5
#>  3 01-701-1028    71   1.4     0  99.3  68.0
#>  4 01-701-1033    74   1.5     0  88.4  54.1
#>  5 01-701-1034    77   1       1  62.6  46.6
#>  6 01-701-1047    85   1       1  67.1  43.6
#>  7 01-701-1097    68   1.4     0  78.0  55.7
#>  8 01-701-1111    81   0.9     1  59.9  46.3
#>  9 01-701-1115    84   1.3     0  78.9  47.2
#> 10 01-701-1118    52   1       0  71.2  87.0
#> # ℹ 244 more rows

`calculate_crcl_peck()`

Calculate Creatinine Clearance (CrCl) using the Peck Formula, a measure of renal function based on age, ideal weight, gender, and serum creatinine

If WTKG [kg] < 120% of IBW [kg], CRCL [mL/min] = CRCL [mL/min] (Cockroft & Gault)
If WTKG [kg] >= 120% of IBW [kg]:
  Males: CRCL [mL/min] = ((140 - AGE [y]) × IBW [kg]) ÷ (72 × SCR [mg/dL])
  Females: CRCL [mL/min] = (((140 - AGE [y]) × IBW [kg]) ÷ (72 × SCR [mg/dL])) × 0.85

This function returns a numeric vector the same length as its inputs. Following are the inputs:

age: Age (y). Numeric vector.
crcl: Creatinine Clearance (mL/min)
ibw: Ideal Body Weight (kg). Numeric vector.
scr: Serum Creatinine (mg/dL). Numeric vector.
sexf: Sex. Numeric vector including values of 0 and/or 1. 0=Male; 1=Female.
wtkg: Weight (kg). Numeric vector.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    CRCLP = calculate_crcl_peck(
      age = AGE,
      crcl = CRCL,
      ibw = IBW,
      scr = SCR,
      sexf = SEXF,
      wtkg = WTKG
    )
  ) %>% 
  select(USUBJID, AGE, CRCL, IBW, SCR, SEXF, WTKG, CRCLP)
#> Formula to calculate CRCL_PECK: 
#>   If WTKG [kg] < 120% of IBW [kg], CRCL [mL/min] = CRCL [mL/min] (Cockroft & Gault)
#>   If WTKG [kg] >= 120% of IBW [kg]:
#>     Males: CRCL [mL/min] = ((140 - AGE [y]) × IBW [kg]) ÷ (72 × SCR [mg/dL])
#>     Females: CRCL [mL/min] = (((140 - AGE [y]) × IBW [kg]) ÷ (72 × SCR [mg/dL])) × 0.85
#> # A tibble: 254 × 8
#>    USUBJID       AGE  CRCL   IBW   SCR  SEXF  WTKG CRCLP
#>    <chr>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 01-701-1015    63  55.0  45.4   0.9     1  54.4  55.0
#>  2 01-701-1023    64  60.5  59.0   1.4     0  80.3  44.5
#>  3 01-701-1028    71  68.0  70.2   1.4     0  99.3  48.0
#>  4 01-701-1033    74  54.1  68.3   1.5     0  88.4  41.7
#>  5 01-701-1034    77  46.6  50.3   1       1  62.6  37.4
#>  6 01-701-1047    85  43.6  46.2   1       1  67.1  30.0
#>  7 01-701-1097    68  55.7  63.7   1.4     0  78.0  45.5
#>  8 01-701-1111    81  46.3  52.5   0.9     1  59.9  46.3
#>  9 01-701-1115    84  47.2  72.9   1.3     0  78.9  47.2
#> 10 01-701-1118    52  87.0  72     1       0  71.2  87.0
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    CRCLP = calculate_crcl_peck()
  ) %>% 
  select(USUBJID, AGE, CRCL, IBW, SCR, SEXF, WTKG, CRCLP)
#> AGE variable found and used for the age argument.
#> CRCL variable found and used for the crcl argument.
#> IBW variable found and used for the ibw argument.
#> SCR variable found and used for the scr argument.
#> SEXF variable found and used for the sexf argument.
#> WTKG variable found and used for the wtkg argument.
#> Formula to calculate CRCL_PECK: 
#>   If WTKG [kg] < 120% of IBW [kg], CRCL [mL/min] = CRCL [mL/min] (Cockroft & Gault)
#>   If WTKG [kg] >= 120% of IBW [kg]:
#>     Males: CRCL [mL/min] = ((140 - AGE [y]) × IBW [kg]) ÷ (72 × SCR [mg/dL])
#>     Females: CRCL [mL/min] = (((140 - AGE [y]) × IBW [kg]) ÷ (72 × SCR [mg/dL])) × 0.85
#> # A tibble: 254 × 8
#>    USUBJID       AGE  CRCL   IBW   SCR  SEXF  WTKG CRCLP
#>    <chr>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 01-701-1015    63  55.0  45.4   0.9     1  54.4  55.0
#>  2 01-701-1023    64  60.5  59.0   1.4     0  80.3  44.5
#>  3 01-701-1028    71  68.0  70.2   1.4     0  99.3  48.0
#>  4 01-701-1033    74  54.1  68.3   1.5     0  88.4  41.7
#>  5 01-701-1034    77  46.6  50.3   1       1  62.6  37.4
#>  6 01-701-1047    85  43.6  46.2   1       1  67.1  30.0
#>  7 01-701-1097    68  55.7  63.7   1.4     0  78.0  45.5
#>  8 01-701-1111    81  46.3  52.5   0.9     1  59.9  46.3
#>  9 01-701-1115    84  47.2  72.9   1.3     0  78.9  47.2
#> 10 01-701-1118    52  87.0  72     1       0  71.2  87.0
#> # ℹ 244 more rows

`calculate_bmi()`

Calculate Body Mass Index (BMI). BMI is a measure of body fat based on height and weight

BMI [kg/m^2] = WTKG [kg] ÷ (HTCM [cm] ÷ 100) ^ 2

This function returns a numeric vector the same length as its inputs. Following are the inputs:

htcm: Height (cm). Numeric vector.
wtkg: Weight (kg). Numeric vector.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    BMI = calculate_bmi(
      htcm = HTCM,
      wtkg = WTKG 
    )
  ) %>% 
  select(USUBJID, HTCM, WTKG, BMI)
#> Formula to calculate BMI: 
#>   BMI [kg/m^2] = WTKG [kg] ÷ (HTCM [cm] ÷ 100) ^ 2
#> # A tibble: 254 × 4
#>    USUBJID      HTCM  WTKG   BMI
#>    <chr>       <dbl> <dbl> <dbl>
#>  1 01-701-1015  147.  54.4  25.1
#>  2 01-701-1023  163.  80.3  30.4
#>  3 01-701-1028  178.  99.3  31.4
#>  4 01-701-1033  175.  88.4  28.8
#>  5 01-701-1034  155.  62.6  26.1
#>  6 01-701-1047  149.  67.1  30.4
#>  7 01-701-1097  169.  78.0  27.3
#>  8 01-701-1111  158.  59.9  23.9
#>  9 01-701-1115  182.  78.9  23.9
#> 10 01-701-1118  180.  71.2  21.9
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    BMI = calculate_bmi()
  ) %>% 
  select(USUBJID, HTCM, WTKG, BMI)
#> HTCM variable found and used for the htcm argument.
#> WTKG variable found and used for the wtkg argument.
#> Formula to calculate BMI: 
#>   BMI [kg/m^2] = WTKG [kg] ÷ (HTCM [cm] ÷ 100) ^ 2
#> # A tibble: 254 × 4
#>    USUBJID      HTCM  WTKG   BMI
#>    <chr>       <dbl> <dbl> <dbl>
#>  1 01-701-1015  147.  54.4  25.1
#>  2 01-701-1023  163.  80.3  30.4
#>  3 01-701-1028  178.  99.3  31.4
#>  4 01-701-1033  175.  88.4  28.8
#>  5 01-701-1034  155.  62.6  26.1
#>  6 01-701-1047  149.  67.1  30.4
#>  7 01-701-1097  169.  78.0  27.3
#>  8 01-701-1111  158.  59.9  23.9
#>  9 01-701-1115  182.  78.9  23.9
#> 10 01-701-1118  180.  71.2  21.9
#> # ℹ 244 more rows

`calculate_bsa()`

Calculate Body Surface Area (BSA). Body Surface Area is a measure of body fat based on height and weight

BSA [m^2] = ((HTCM [cm]) ^ 0.42246 * (WTKG [kg]) ^ 0.51456) * 0.0235

This function returns a numeric vector the same length as its inputs. Following are the inputs:

htcm: Height (cm). Numeric vector.
wtkg: Weight (kg). Numeric vector.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    BSA = calculate_bsa(
      htcm = HTCM, 
      wtkg = WTKG
    )
  ) %>% 
  select(USUBJID, HTCM, WTKG, BSA)
#> Formula to calculate BSA: 
#>   BSA [m^2] = ((HTCM [cm]) ^ 0.42246 * (WTKG [kg]) ^ 0.51456) * 0.0235
#> # A tibble: 254 × 4
#>    USUBJID      HTCM  WTKG   BSA
#>    <chr>       <dbl> <dbl> <dbl>
#>  1 01-701-1015  147.  54.4  1.51
#>  2 01-701-1023  163.  80.3  1.93
#>  3 01-701-1028  178.  99.3  2.23
#>  4 01-701-1033  175.  88.4  2.09
#>  5 01-701-1034  155.  62.6  1.66
#>  6 01-701-1047  149.  67.1  1.69
#>  7 01-701-1097  169.  78.0  1.93
#>  8 01-701-1111  158.  59.9  1.64
#>  9 01-701-1115  182.  78.9  2.00
#> 10 01-701-1118  180.  71.2  1.89
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    BSA = calculate_bsa()
  ) %>% 
  select(USUBJID, HTCM, WTKG, BSA)
#> HTCM variable found and used for the htcm argument.
#> WTKG variable found and used for the wtkg argument.
#> Formula to calculate BSA: 
#>   BSA [m^2] = ((HTCM [cm]) ^ 0.42246 * (WTKG [kg]) ^ 0.51456) * 0.0235
#> # A tibble: 254 × 4
#>    USUBJID      HTCM  WTKG   BSA
#>    <chr>       <dbl> <dbl> <dbl>
#>  1 01-701-1015  147.  54.4  1.51
#>  2 01-701-1023  163.  80.3  1.93
#>  3 01-701-1028  178.  99.3  2.23
#>  4 01-701-1033  175.  88.4  2.09
#>  5 01-701-1034  155.  62.6  1.66
#>  6 01-701-1047  149.  67.1  1.69
#>  7 01-701-1097  169.  78.0  1.93
#>  8 01-701-1111  158.  59.9  1.64
#>  9 01-701-1115  182.  78.9  2.00
#> 10 01-701-1118  180.  71.2  1.89
#> # ℹ 244 more rows

`calculate_lbm()`

Calculate Lean Body Mass (LBM), an estimation of how much someone weighs without the body fat; how much a persons bones, organs and muscles weigh

Males: LBM [kg] = (1.10 × WTKG [kg]) - 128 × ((WTKG [kg]) ^ 2 ÷ (HTCM [cm]) ^ 2)
Females: LBM [kg] = (1.07 × WTKG [kg]) - 148 × ((WTKG [kg]) ^ 2 ÷ (HTCM [cm]) ^ 2)

This function returns a numeric vector the same length as its inputs. Following are the inputs:

htcm: Height (cm). Numeric vector.
sexf: Sex. Numeric vector including values of 0 and/or 1. 0=Male; 1=Female.
wtkg: Weight (kg). Numeric vector.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    LBM = calculate_lbm(
      htcm = HTCM,
      sexf = SEXF,
      wtkg = WTKG
    )
  ) %>% 
  select(USUBJID, HTCM, SEXF, WTKG, LBM)
#> Formula to calculate LBM: 
#>   Males: LBM [kg] = (1.10 × WTKG [kg]) - 128 × ((WTKG [kg]) ^ 2 ÷ (HTCM [cm]) ^ 2)
#>   Females: LBM [kg] = (1.07 × WTKG [kg]) - 148 × ((WTKG [kg]) ^ 2 ÷ (HTCM [cm]) ^ 2)
#> # A tibble: 254 × 5
#>    USUBJID      HTCM  SEXF  WTKG   LBM
#>    <chr>       <dbl> <dbl> <dbl> <dbl>
#>  1 01-701-1015  147.     1  54.4  38.0
#>  2 01-701-1023  163.     0  80.3  57.1
#>  3 01-701-1028  178.     0  99.3  69.3
#>  4 01-701-1033  175.     0  88.4  64.7
#>  5 01-701-1034  155.     1  62.6  42.8
#>  6 01-701-1047  149.     1  67.1  41.6
#>  7 01-701-1097  169.     0  78.0  58.5
#>  8 01-701-1111  158.     1  59.9  42.9
#>  9 01-701-1115  182.     0  78.9  62.6
#> 10 01-701-1118  180.     0  71.2  58.4
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    LBM = calculate_lbm()
  ) %>% 
  select(USUBJID, HTCM, SEXF, WTKG, LBM)
#> HTCM variable found and used for the htcm argument.
#> SEXF variable found and used for the sexf argument.
#> WTKG variable found and used for the wtkg argument.
#> Formula to calculate LBM: 
#>   Males: LBM [kg] = (1.10 × WTKG [kg]) - 128 × ((WTKG [kg]) ^ 2 ÷ (HTCM [cm]) ^ 2)
#>   Females: LBM [kg] = (1.07 × WTKG [kg]) - 148 × ((WTKG [kg]) ^ 2 ÷ (HTCM [cm]) ^ 2)
#> # A tibble: 254 × 5
#>    USUBJID      HTCM  SEXF  WTKG   LBM
#>    <chr>       <dbl> <dbl> <dbl> <dbl>
#>  1 01-701-1015  147.     1  54.4  38.0
#>  2 01-701-1023  163.     0  80.3  57.1
#>  3 01-701-1028  178.     0  99.3  69.3
#>  4 01-701-1033  175.     0  88.4  64.7
#>  5 01-701-1034  155.     1  62.6  42.8
#>  6 01-701-1047  149.     1  67.1  41.6
#>  7 01-701-1097  169.     0  78.0  58.5
#>  8 01-701-1111  158.     1  59.9  42.9
#>  9 01-701-1115  182.     0  78.9  62.6
#> 10 01-701-1118  180.     0  71.2  58.4
#> # ℹ 244 more rows

`calculate_ibw()`

Calculate Ideal Body Weight (IBW), a measure of potential body fat based on height

Males: IBW [kg] = 51.65 [kg] + 1.85 [kg] × ((HTCM [cm] ÷ 2.54) - 60)
Females: IBW [kg] = 48.67 [kg] + 1.65 [kg] × ((HTCM [cm] ÷ 2.54) - 60)

This function returns a numeric vector the same length as its inputs. Following are the inputs:

htcm: Height (cm). Numeric vector.
sexf: Sex. Numeric vector including values of 0 and/or 1. 0=Male; 1=Female.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    IBW = calculate_ibw(
      htcm = HTCM,
      sexf = SEXF 
    )
  ) %>% 
  select(USUBJID, HTCM, SEXF, IBW)
#> Formula to calculate IBW: 
#>   Males: IBW [kg] = 51.65 [kg] + 1.85 [kg] × ((HTCM [cm] ÷ 2.54) - 60)
#>   Females: IBW [kg] = 48.67 [kg] + 1.65 [kg] × ((HTCM [cm] ÷ 2.54) - 60)
#> # A tibble: 254 × 4
#>    USUBJID      HTCM  SEXF   IBW
#>    <chr>       <dbl> <dbl> <dbl>
#>  1 01-701-1015  147.     1  45.4
#>  2 01-701-1023  163.     0  59.0
#>  3 01-701-1028  178.     0  70.2
#>  4 01-701-1033  175.     0  68.3
#>  5 01-701-1034  155.     1  50.3
#>  6 01-701-1047  149.     1  46.2
#>  7 01-701-1097  169.     0  63.7
#>  8 01-701-1111  158.     1  52.5
#>  9 01-701-1115  182.     0  72.9
#> 10 01-701-1118  180.     0  72  
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    IBW = calculate_ibw()
  ) %>% 
  select(USUBJID, HTCM, SEXF, IBW)
#> HTCM variable found and used for the htcm argument.
#> SEXF variable found and used for the sexf argument.
#> Formula to calculate IBW: 
#>   Males: IBW [kg] = 51.65 [kg] + 1.85 [kg] × ((HTCM [cm] ÷ 2.54) - 60)
#>   Females: IBW [kg] = 48.67 [kg] + 1.65 [kg] × ((HTCM [cm] ÷ 2.54) - 60)
#> # A tibble: 254 × 4
#>    USUBJID      HTCM  SEXF   IBW
#>    <chr>       <dbl> <dbl> <dbl>
#>  1 01-701-1015  147.     1  45.4
#>  2 01-701-1023  163.     0  59.0
#>  3 01-701-1028  178.     0  70.2
#>  4 01-701-1033  175.     0  68.3
#>  5 01-701-1034  155.     1  50.3
#>  6 01-701-1047  149.     1  46.2
#>  7 01-701-1097  169.     0  63.7
#>  8 01-701-1111  158.     1  52.5
#>  9 01-701-1115  182.     0  72.9
#> 10 01-701-1118  180.     0  72  
#> # ℹ 244 more rows

`calculate_ibw_child()`

Calculate Ideal Body Weight (for children), a measure of potential body fat based on height

IBW [kg] = [(HTCM [cm]) ^ 2 × 1.65] ÷ 1000

This function returns a numeric vector the same length as its inputs. Following is the input:

age: Age (y). Numeric vector.
htcm: Height (cm). Numeric vector.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    IBWCHILD = calculate_ibw_child(age = AGE, htcm = HTCM)
  ) %>% 
  select(USUBJID, AGE, HTCM, IBWCHILD)
#> Formula to calculate IBW_CHILD: 
#>   IBW [kg] = [(HTCM [cm]) ^ 2 × 1.65] ÷ 1000
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `IBWCHILD = calculate_ibw_child(age = AGE, htcm = HTCM)`.
#> Caused by warning:
#> ! Cases where AGE > 18 were detected. This formula is intended for children.
#> # A tibble: 254 × 4
#>    USUBJID       AGE  HTCM IBWCHILD
#>    <chr>       <dbl> <dbl>    <dbl>
#>  1 01-701-1015    63  147.     35.8
#>  2 01-701-1023    64  163.     43.6
#>  3 01-701-1028    71  178.     52.2
#>  4 01-701-1033    74  175.     50.7
#>  5 01-701-1034    77  155.     39.6
#>  6 01-701-1047    85  149.     36.4
#>  7 01-701-1097    68  169.     47.1
#>  8 01-701-1111    81  158.     41.3
#>  9 01-701-1115    84  182.     54.4
#> 10 01-701-1118    52  180.     53.7
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    IBWCHILD = calculate_ibw_child()
  ) %>% 
  select(USUBJID, AGE, HTCM, IBWCHILD)
#> AGE variable found and used for the age argument.
#> HTCM variable found and used for the htcm argument.
#> Formula to calculate IBW_CHILD: 
#>   IBW [kg] = [(HTCM [cm]) ^ 2 × 1.65] ÷ 1000
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `IBWCHILD = calculate_ibw_child()`.
#> Caused by warning:
#> ! Cases where AGE > 18 were detected. This formula is intended for children.
#> # A tibble: 254 × 4
#>    USUBJID       AGE  HTCM IBWCHILD
#>    <chr>       <dbl> <dbl>    <dbl>
#>  1 01-701-1015    63  147.     35.8
#>  2 01-701-1023    64  163.     43.6
#>  3 01-701-1028    71  178.     52.2
#>  4 01-701-1033    74  175.     50.7
#>  5 01-701-1034    77  155.     39.6
#>  6 01-701-1047    85  149.     36.4
#>  7 01-701-1097    68  169.     47.1
#>  8 01-701-1111    81  158.     41.3
#>  9 01-701-1115    84  182.     54.4
#> 10 01-701-1118    52  180.     53.7
#> # ℹ 244 more rows

`calculate_egfr()`

Calculate Estimated Glomerular Filtration Rate (eGFR) for adults

EGFR [mL/min/1.73m^2] = 175 × (SCR [mg/dL] ^ -1.154) × (AGE [y] ^ -0.203) × (0.742 if female) × (1.212 if African American)

This function returns a numeric vector the same length as its inputs. Following are the inputs:

age: Age (y). Numeric vector.
racen: Race. Numeric vector where the value 2 is expected to correspond to Black/African American.
scr: Serum Creatinine (mg/dL). Numeric vector.
sexf: Sex. Numeric vector including values of 0 and/or 1. 0=Male; 1=Female.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    EGFR = calculate_egfr(
      age = AGE,
      racen = RACEN,
      scr = SCR,
      sexf = SEXF
    )
  ) %>% 
  select(USUBJID, AGE, RACEN, SCR, SEXF, EGFR)
#> Formula to calculate EGFR: 
#>   EGFR [mL/min/1.73m^2] = 175 × (SCR [mg/dL] ^ -1.154) × (AGE [y] ^ -0.203) × (0.742 if female) × (1.212 if African American)
#> # A tibble: 254 × 6
#>    USUBJID       AGE RACEN   SCR  SEXF  EGFR
#>    <chr>       <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 01-701-1015    63     1   0.9     1  63.2
#>  2 01-701-1023    64     1   1.4     0  51.0
#>  3 01-701-1028    71     1   1.4     0  50.0
#>  4 01-701-1033    74     1   1.5     0  45.7
#>  5 01-701-1034    77     1   1       1  53.8
#>  6 01-701-1047    85     1   1       1  52.7
#>  7 01-701-1097    68     1   1.4     0  50.4
#>  8 01-701-1111    81     1   0.9     1  60.1
#>  9 01-701-1115    84     1   1.3     0  52.6
#> 10 01-701-1118    52     1   1       0  78.5
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    EGFR = calculate_egfr()
  ) %>% 
  select(USUBJID, AGE, RACEN, SCR, SEXF, EGFR)
#> AGE variable found and used for the age argument.
#> RACEN variable found and used for the racen argument.
#> SCR variable found and used for the scr argument.
#> SEXF variable found and used for the sexf argument.
#> Formula to calculate EGFR: 
#>   EGFR [mL/min/1.73m^2] = 175 × (SCR [mg/dL] ^ -1.154) × (AGE [y] ^ -0.203) × (0.742 if female) × (1.212 if African American)
#> # A tibble: 254 × 6
#>    USUBJID       AGE RACEN   SCR  SEXF  EGFR
#>    <chr>       <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 01-701-1015    63     1   0.9     1  63.2
#>  2 01-701-1023    64     1   1.4     0  51.0
#>  3 01-701-1028    71     1   1.4     0  50.0
#>  4 01-701-1033    74     1   1.5     0  45.7
#>  5 01-701-1034    77     1   1       1  53.8
#>  6 01-701-1047    85     1   1       1  52.7
#>  7 01-701-1097    68     1   1.4     0  50.4
#>  8 01-701-1111    81     1   0.9     1  60.1
#>  9 01-701-1115    84     1   1.3     0  52.6
#> 10 01-701-1118    52     1   1       0  78.5
#> # ℹ 244 more rows

`calculate_egfr_child()`

Calculate Estimated Glomerular Filtration Rate (eGFR) for Pedatrics using the Schwartz formula

Children (AGE [y] <= 16), where:
EGFR = (k * HTCM [cm]) / SCR [mg/dL]
k = 0.45 for AGE [y] <= 1 (full term)
k = 0.55 for girls 1 < AGE [y] <= 16
k = 0.55 for boys 1 < AGE [y] < 13
k = 0.70 for boys 13 <= AGE [y] <= 16

This function returns a numeric vector the same length as its inputs. Following are the inputs:

age: Age (y). Numeric vector.
htcm: Height (cm). Numeric vector.
scr: Serum Creatinine (mg/dL). Numeric vector.
sexf: Sex. Numeric vector including values of 0 and/or 1. 0=Male; 1=Female.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    EGFR_CHILD = calculate_egfr_child(
      age = AGE, 
      htcm = HTCM,
      scr = SCR,
      sexf = SEXF
    )
  ) %>% 
  select(USUBJID, AGE, HTCM, SCR, SEXF, EGFR_CHILD)
#> Formula to calculate EGFR_CHILD: 
#>   Children (AGE [y] <= 16), where:
#>   EGFR = (k * HTCM [cm]) / SCR [mg/dL]
#>   k = 0.45 for AGE [y] <= 1 (full term)
#>   k = 0.55 for girls 1 < AGE [y] <= 16
#>   k = 0.55 for boys 1 < AGE [y] < 13
#>   k = 0.70 for boys 13 <= AGE [y] <= 16
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `EGFR_CHILD = calculate_egfr_child(age = AGE, htcm = HTCM, scr =
#>   SCR, sexf = SEXF)`.
#> Caused by warning:
#> ! Cases where AGE > 16 were detected. This formula is intended for children.
#> # A tibble: 254 × 6
#>    USUBJID       AGE  HTCM   SCR  SEXF EGFR_CHILD
#>    <chr>       <dbl> <dbl> <dbl> <dbl>      <dbl>
#>  1 01-701-1015    63  147.   0.9     1         NA
#>  2 01-701-1023    64  163.   1.4     0         NA
#>  3 01-701-1028    71  178.   1.4     0         NA
#>  4 01-701-1033    74  175.   1.5     0         NA
#>  5 01-701-1034    77  155.   1       1         NA
#>  6 01-701-1047    85  149.   1       1         NA
#>  7 01-701-1097    68  169.   1.4     0         NA
#>  8 01-701-1111    81  158.   0.9     1         NA
#>  9 01-701-1115    84  182.   1.3     0         NA
#> 10 01-701-1118    52  180.   1       0         NA
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    EGFR_CHILD = calculate_egfr_child()
  ) %>% 
  select(USUBJID, AGE, HTCM, SCR, SEXF, EGFR_CHILD)
#> AGE variable found and used for the age argument.
#> HTCM variable found and used for the htcm argument.
#> SCR variable found and used for the scr argument.
#> SEXF variable found and used for the sexf argument.
#> Formula to calculate EGFR_CHILD: 
#>   Children (AGE [y] <= 16), where:
#>   EGFR = (k * HTCM [cm]) / SCR [mg/dL]
#>   k = 0.45 for AGE [y] <= 1 (full term)
#>   k = 0.55 for girls 1 < AGE [y] <= 16
#>   k = 0.55 for boys 1 < AGE [y] < 13
#>   k = 0.70 for boys 13 <= AGE [y] <= 16
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `EGFR_CHILD = calculate_egfr_child()`.
#> Caused by warning:
#> ! Cases where AGE > 16 were detected. This formula is intended for children.
#> # A tibble: 254 × 6
#>    USUBJID       AGE  HTCM   SCR  SEXF EGFR_CHILD
#>    <chr>       <dbl> <dbl> <dbl> <dbl>      <dbl>
#>  1 01-701-1015    63  147.   0.9     1         NA
#>  2 01-701-1023    64  163.   1.4     0         NA
#>  3 01-701-1028    71  178.   1.4     0         NA
#>  4 01-701-1033    74  175.   1.5     0         NA
#>  5 01-701-1034    77  155.   1       1         NA
#>  6 01-701-1047    85  149.   1       1         NA
#>  7 01-701-1097    68  169.   1.4     0         NA
#>  8 01-701-1111    81  158.   0.9     1         NA
#>  9 01-701-1115    84  182.   1.3     0         NA
#> 10 01-701-1118    52  180.   1       0         NA
#> # ℹ 244 more rows

`calculate_rfcat()`

Calculate Renal Function Category (RFCAT)

1 = Normal Function (>= 90 mL/min)
2 = Mild Impairment (60-89 mL/min)
3 = Moderate Impairment (30-59 mL/min)
4 = Severe Impairment (15-29 mL/min)
5 = End Stage Disease (< 15 mL/min or Dialysis)

This function returns a numeric vector the same length as its inputs. Following is the input:

egfr: Estimated Glomerular Filtration Rate (mL/min/1.73m^2). Numeric vector.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    EGFR = calculate_egfr(
      age = AGE,
      racen = RACEN,
      scr = SCR,
      sexf = SEXF
    ),
    RFCAT = calculate_rfcat(egfr = EGFR)
  ) %>% 
  select(USUBJID, EGFR, RFCAT)
#> Formula to calculate EGFR: 
#>   EGFR [mL/min/1.73m^2] = 175 × (SCR [mg/dL] ^ -1.154) × (AGE [y] ^ -0.203) × (0.742 if female) × (1.212 if African American)
#> Formula to calculate RFCAT: 
#>   1 = Normal Function (>= 90 mL/min)
#>   2 = Mild Impairment (60-89 mL/min)
#>   3 = Moderate Impairment (30-59 mL/min)
#>   4 = Severe Impairment (15-29 mL/min)
#>   5 = End Stage Disease (< 15 mL/min or Dialysis)
#> # A tibble: 254 × 3
#>    USUBJID      EGFR RFCAT
#>    <chr>       <dbl> <dbl>
#>  1 01-701-1015  63.2     2
#>  2 01-701-1023  51.0     3
#>  3 01-701-1028  50.0     3
#>  4 01-701-1033  45.7     3
#>  5 01-701-1034  53.8     3
#>  6 01-701-1047  52.7     3
#>  7 01-701-1097  50.4     3
#>  8 01-701-1111  60.1     2
#>  9 01-701-1115  52.6     3
#> 10 01-701-1118  78.5     2
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    RFCAT = calculate_rfcat()
  ) %>% 
  select(USUBJID, EGFR, RFCAT)
#> EGFR variable found and used for the egfr argument.
#> Formula to calculate RFCAT: 
#>   1 = Normal Function (>= 90 mL/min)
#>   2 = Mild Impairment (60-89 mL/min)
#>   3 = Moderate Impairment (30-59 mL/min)
#>   4 = Severe Impairment (15-29 mL/min)
#>   5 = End Stage Disease (< 15 mL/min or Dialysis)
#> # A tibble: 254 × 3
#>    USUBJID      EGFR RFCAT
#>    <chr>       <dbl> <dbl>
#>  1 01-701-1015  63.2     2
#>  2 01-701-1023  51.0     3
#>  3 01-701-1028  50.0     3
#>  4 01-701-1033  45.7     3
#>  5 01-701-1034  53.8     3
#>  6 01-701-1047  52.7     3
#>  7 01-701-1097  50.4     3
#>  8 01-701-1111  60.1     2
#>  9 01-701-1115  52.6     3
#> 10 01-701-1118  78.5     2
#> # ℹ 244 more rows

`calculate_tbilcat`()

Calculate Total Bilirubin Category

if TBIL [mg/dL] <= ULN [mg/dL] then TBILCAT = 0
if ULN [mg/dL] < TBIL [mg/dL] <= 1.5 × ULN [mg/dL] then TBILCAT = 1
if 1.5 × ULN [mg/dL] < TBIL [mg/dL] <= 3 × ULN [mg/dL] then TBILCAT = 2
if TBIL [mg/dL] > 3 × ULN [mg/dL] then TBILCAT = 3

This function returns a numeric vector the same length as its inputs. Following are the inputs:

tbil: Total Bilirubin (mg/dL). Numeric vector.
tbiluln: Reference Range Upper Limit (same units as observed values). Numeric vector.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    TBILCAT = calculate_tbilcat(
      tbil = TBIL, 
      tbiluln = TBILULN
    )
  ) %>% 
  select(USUBJID, TBIL, TBILULN, TBILCAT)
#> Formula to calculate TBILCAT: 
#>   if TBIL [mg/dL] <= ULN [mg/dL] then TBILCAT = 0
#>   if ULN [mg/dL] < TBIL [mg/dL] <= 1.5 × ULN [mg/dL] then TBILCAT = 1
#>   if 1.5 × ULN [mg/dL] < TBIL [mg/dL] <= 3 × ULN [mg/dL] then TBILCAT = 2
#>   if TBIL [mg/dL] > 3 × ULN [mg/dL] then TBILCAT = 3
#> # A tibble: 254 × 4
#>    USUBJID      TBIL TBILULN TBILCAT
#>    <chr>       <dbl>   <dbl>   <dbl>
#>  1 01-701-1015   0.6     1.2       0
#>  2 01-701-1023   0.7     1.2       0
#>  3 01-701-1028   1.1     1.2       0
#>  4 01-701-1033   0.8     1.2       0
#>  5 01-701-1034   0.6     1.2       0
#>  6 01-701-1047   0.4     1.2       0
#>  7 01-701-1097   0.4     1.2       0
#>  8 01-701-1111   0.4     1.2       0
#>  9 01-701-1115   0.5     1.2       0
#> 10 01-701-1118   0.7     1.2       0
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    TBILCAT = calculate_tbilcat()
  ) %>% 
  select(USUBJID, TBIL, TBILULN, TBILCAT)
#> TBIL variable found and used for the tbil argument.
#> TBILULN variable found and used for the tbiluln argument.
#> Formula to calculate TBILCAT: 
#>   if TBIL [mg/dL] <= ULN [mg/dL] then TBILCAT = 0
#>   if ULN [mg/dL] < TBIL [mg/dL] <= 1.5 × ULN [mg/dL] then TBILCAT = 1
#>   if 1.5 × ULN [mg/dL] < TBIL [mg/dL] <= 3 × ULN [mg/dL] then TBILCAT = 2
#>   if TBIL [mg/dL] > 3 × ULN [mg/dL] then TBILCAT = 3
#> # A tibble: 254 × 4
#>    USUBJID      TBIL TBILULN TBILCAT
#>    <chr>       <dbl>   <dbl>   <dbl>
#>  1 01-701-1015   0.6     1.2       0
#>  2 01-701-1023   0.7     1.2       0
#>  3 01-701-1028   1.1     1.2       0
#>  4 01-701-1033   0.8     1.2       0
#>  5 01-701-1034   0.6     1.2       0
#>  6 01-701-1047   0.4     1.2       0
#>  7 01-701-1097   0.4     1.2       0
#>  8 01-701-1111   0.4     1.2       0
#>  9 01-701-1115   0.5     1.2       0
#> 10 01-701-1118   0.7     1.2       0
#> # ℹ 244 more rows

`calculate_astcat()`

Calculate Aspartate Aminotransferase Category

if AST [U/L] <= ULN [U/L] then ASTCAT = 0
if ULN [U/L] < AST [U/L] then ASTCAT = 1

This function returns a numeric vector the same length as its inputs. Following are the inputs:

ast: Aspartate Aminotransferase (U/L). Numeric vector.
astuln: Reference Range Upper Limit (same units as observed values). Numeric vector.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    ASTCAT = calculate_astcat(
      ast = AST, 
      astuln = ASTULN
    )
  ) %>% 
  select(USUBJID, AST, ASTULN, ASTCAT)
#> Formula to calculate ASTCAT: 
#>   if AST [U/L] <= ULN [U/L] then ASTCAT = 0
#>   if ULN [U/L] < AST [U/L] then ASTCAT = 1
#> # A tibble: 254 × 4
#>    USUBJID       AST ASTULN ASTCAT
#>    <chr>       <dbl>  <dbl>  <dbl>
#>  1 01-701-1015    40     34      1
#>  2 01-701-1023    21     36      0
#>  3 01-701-1028    24     36      0
#>  4 01-701-1033    20     36      0
#>  5 01-701-1034    23     34      0
#>  6 01-701-1047    25     34      0
#>  7 01-701-1097    19     36      0
#>  8 01-701-1111    28     34      0
#>  9 01-701-1115    26     36      0
#> 10 01-701-1118    15     36      0
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    ASTCAT = calculate_astcat()
  ) %>% 
  select(USUBJID, AST, ASTULN, ASTCAT)
#> AST variable found and used for the ast argument.
#> ASTULN variable found and used for the astuln argument.
#> Formula to calculate ASTCAT: 
#>   if AST [U/L] <= ULN [U/L] then ASTCAT = 0
#>   if ULN [U/L] < AST [U/L] then ASTCAT = 1
#> # A tibble: 254 × 4
#>    USUBJID       AST ASTULN ASTCAT
#>    <chr>       <dbl>  <dbl>  <dbl>
#>  1 01-701-1015    40     34      1
#>  2 01-701-1023    21     36      0
#>  3 01-701-1028    24     36      0
#>  4 01-701-1033    20     36      0
#>  5 01-701-1034    23     34      0
#>  6 01-701-1047    25     34      0
#>  7 01-701-1097    19     36      0
#>  8 01-701-1111    28     34      0
#>  9 01-701-1115    26     36      0
#> 10 01-701-1118    15     36      0
#> # ℹ 244 more rows

`calculate_nciliv()`

Calculate NCI Liver Function Group (NCILIV)

if TBILCAT = 0 and ASTCAT = 0 then NCILIV = 0
if TBILCAT = 0 and ASTCAT = 1 then NCILIV = 1
if TBILCAT = 1 then NCILIV = 2
if TBILCAT = 2 then NCILIV = 3
if TBILCAT = 3 then NCILIV = 4

This function returns a numeric vector the same length as its inputs. Following are the inputs:

astcat: Aspartate Aminotransferase Category. Numeric vector.
tbilcat: Total Bilirubin Category. Numeric vector.

Examples

All expected variables are provided:

dmcognigen_cov %>% 
  mutate(
    NCILIV = calculate_nciliv(astcat = ASTCAT, tbilcat = TBILCAT)
  ) %>% 
  select(USUBJID, ASTCAT, TBILCAT, NCILIV)
#> Formula to calculate NCILIV: 
#>   if TBILCAT = 0 and ASTCAT = 0 then NCILIV = 0
#>   if TBILCAT = 0 and ASTCAT = 1 then NCILIV = 1
#>   if TBILCAT = 1 then NCILIV = 2
#>   if TBILCAT = 2 then NCILIV = 3
#>   if TBILCAT = 3 then NCILIV = 4
#> # A tibble: 254 × 4
#>    USUBJID     ASTCAT TBILCAT NCILIV
#>    <chr>        <dbl>   <dbl>  <dbl>
#>  1 01-701-1015      1       0      1
#>  2 01-701-1023      0       0      0
#>  3 01-701-1028      0       0      0
#>  4 01-701-1033      0       0      0
#>  5 01-701-1034      0       0      0
#>  6 01-701-1047      0       0      0
#>  7 01-701-1097      0       0      0
#>  8 01-701-1111      0       0      0
#>  9 01-701-1115      0       0      0
#> 10 01-701-1118      0       0      0
#> # ℹ 244 more rows

All or some of expected variables are missing. In this case, the function will try to detect those missing variables from the parent environment:

dmcognigen_cov %>% 
  mutate(
    NCILIV = calculate_nciliv()
  ) %>% 
  select(USUBJID, ASTCAT, TBILCAT, NCILIV)
#> ASTCAT variable found and used for the astcat argument.
#> TBILCAT variable found and used for the tbilcat argument.
#> Formula to calculate NCILIV: 
#>   if TBILCAT = 0 and ASTCAT = 0 then NCILIV = 0
#>   if TBILCAT = 0 and ASTCAT = 1 then NCILIV = 1
#>   if TBILCAT = 1 then NCILIV = 2
#>   if TBILCAT = 2 then NCILIV = 3
#>   if TBILCAT = 3 then NCILIV = 4
#> # A tibble: 254 × 4
#>    USUBJID     ASTCAT TBILCAT NCILIV
#>    <chr>        <dbl>   <dbl>  <dbl>
#>  1 01-701-1015      1       0      1
#>  2 01-701-1023      0       0      0
#>  3 01-701-1028      0       0      0
#>  4 01-701-1033      0       0      0
#>  5 01-701-1034      0       0      0
#>  6 01-701-1047      0       0      0
#>  7 01-701-1097      0       0      0
#>  8 01-701-1111      0       0      0
#>  9 01-701-1115      0       0      0
#> 10 01-701-1118      0       0      0
#> # ℹ 244 more rows

Create standard variables

Detect missing variables from environment

Source datasets

dmcognigen_cov

dmcognigen_conc

Functions

calculate_crcl()

Examples

calculate_crcl_peck()

Examples

calculate_bmi()

Examples

calculate_bsa()

Examples

calculate_lbm()

Examples

calculate_ibw()

Examples

calculate_ibw_child()

Examples

calculate_egfr()

Examples

calculate_egfr_child()

Examples

calculate_rfcat()

Examples

calculate_tbilcat()

Examples

calculate_astcat()

Examples

calculate_nciliv()

Examples

`dmcognigen_cov`

`dmcognigen_conc`

`calculate_crcl()`

`calculate_crcl_peck()`

`calculate_bmi()`

`calculate_bsa()`

`calculate_lbm()`

`calculate_ibw()`

`calculate_ibw_child()`

`calculate_egfr()`

`calculate_egfr_child()`

`calculate_rfcat()`

`calculate_tbilcat`()

`calculate_astcat()`

`calculate_nciliv()`