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Derive Anthropometric indicators (Z-Scores/Percentiles-for-Age) based on Standard Growth Charts

Source: R/derive_params_growth_age.R
derive_params_growth_age.Rd

Derive Anthropometric indicators (Z-Scores/Percentiles-for-Age) based on Standard Growth Charts for Height/Weight/BMI/Head Circumference by Age

Usage

derive_params_growth_age(
  dataset,
  sex,
  age,
  age_unit,
  meta_criteria,
  parameter,
  analysis_var,
  bmi_cdc_correction = FALSE,
  who_correction = FALSE,
  set_values_to_sds = NULL,
  set_values_to_pctl = NULL
)

Arguments

dataset

Input dataset

The variables specified in sex, age, age_unit, parameter, analysis_var are expected to be in the dataset.

sex

Sex

A character vector is expected.

Expected Values: M, F

age

Current Age

A numeric vector is expected. Note that this is the actual age at the current visit.

age_unit

Age Unit A character vector is expected.

Expected values: days, weeks, months

meta_criteria

Metadata dataset

A metadata dataset with the following expected variables: AGE, AGEU, SEX, L, M, S

The dataset can be derived from CDC/WHO or user-defined datasets. The CDC/WHO growth chart metadata datasets are available in the package and will require small modifications.

If the age value from dataset falls between two AGE values in meta_criteria, then the L/M/S values that are chosen/mapped will be the AGE that has the smaller absolute difference to the value in age. e.g. If dataset has a current age of 27.49 months, and the metadata contains records for 27 and 28 months, the L/M/S corresponding to the 27 months record will be used.

  • AGE - Age

  • AGEU - Age Unit

  • SEX - Sex

  • L - Power in the Box-Cox transformation to normality

  • M - Median

  • S - Coefficient of variation

parameter

Anthropometric measurement parameter to calculate z-score or percentile

A condition is expected with the input dataset VSTESTCD/PARAMCD for which we want growth derivations:

e.g. parameter = VSTESTCD == "WEIGHT".

There is CDC/WHO metadata available for Height, Weight, BMI, and Head Circumference available in the admiralpeds package.

analysis_var

Variable containing anthropometric measurement

A numeric vector is expected, e.g. AVAL, VSSTRESN

bmi_cdc_correction

Extended CDC BMI-for-age correction

A logical scalar, e.g. TRUE/FALSE is expected. CDC developed extended percentiles (>95%) to monitor high BMI values, if set to TRUE the CDC's correction is applied.

who_correction

WHO adjustment for weight-based indicators

A logical scalar, e.g. TRUE/FALSE is expected. WHO constructed a restricted application of the LMS method for weight-based indicators. More details on these exact rules applied can be found at the document page 302 of the WHO Child Growth Standards Guidelines. If set to TRUE the WHO correction is applied.

set_values_to_sds

Variables to be set for Z-Scores

The specified variables are set to the specified values for the new observations. For example, set_values_to_sds(exprs(PARAMCD = "BMIASDS", PARAM = "BMI-for-age z-score")) defines the parameter code and parameter.

The formula to calculate the Z-score is as follows:

$$\frac{((\frac{obs}{M})^L - 1)}{L * S}$$

where "obs" is the observed value for the respective anthropometric measure being calculated.

Permitted Values: List of variable-value pairs

If left as default value, NULL, then parameter not derived in output dataset

set_values_to_pctl

Variables to be set for Percentile

The specified variables are set to the specified values for the new observations. For example, set_values_to_pctl(exprs(PARAMCD = "BMIAPCTL", PARAM = "BMI-for-age percentile")) defines the parameter code and parameter.

Permitted Values: List of variable-value pair

If left as default value, NULL, then parameter not derived in output dataset

Value

The input dataset additional records with the new parameter added.

See also

Vital Signs Functions for adding Parameters/Records derive_params_growth_height()

Examples

library(dplyr, warn.conflicts = FALSE)
library(lubridate, warn.conflicts = FALSE)
library(rlang, warn.conflicts = FALSE)
library(admiral, warn.conflicts = FALSE)

advs <- dm_peds %>%
  select(USUBJID, BRTHDTC, SEX) %>%
  right_join(., vs_peds, by = "USUBJID") %>%
  mutate(
    VSDT = ymd(VSDTC),
    BRTHDT = ymd(BRTHDTC)
  ) %>%
  derive_vars_duration(
    new_var = AGECUR_D,
    new_var_unit = CURU_D,
    start_date = BRTHDT,
    end_date = VSDT,
    out_unit = "days",
    trunc_out = FALSE
  ) %>%
  derive_vars_duration(
    new_var = AGECUR_M,
    new_var_unit = CURU_M,
    start_date = BRTHDT,
    end_date = VSDT,
    out_unit = "months",
    trunc_out = FALSE
  ) %>%
  mutate(
    AGECUR = ifelse(AGECUR_D >= 365.25 * 2, AGECUR_M, AGECUR_D),
    AGECURU = ifelse(AGECUR_D >= 365.25 * 2, CURU_M, CURU_D)
  )

# metadata is in months
cdc_meta_criteria <- admiralpeds::cdc_htage %>%
  mutate(
    age_unit = "months",
    SEX = ifelse(SEX == 1, "M", "F")
  )

# metadata is in days
who_meta_criteria <- bind_rows(
  (admiralpeds::who_lgth_ht_for_age_boys %>%
    mutate(
      SEX = "M",
      age_unit = "days"
    )
  ),
  (admiralpeds::who_lgth_ht_for_age_girls %>%
    mutate(
      SEX = "F",
      age_unit = "days"
    )
  )
) %>%
  rename(AGE = Day)

criteria <- bind_rows(
  cdc_meta_criteria,
  who_meta_criteria
) %>%
  rename(AGEU = age_unit)

derive_params_growth_age(
  advs,
  sex = SEX,
  age = AGECUR,
  age_unit = AGECURU,
  meta_criteria = criteria,
  parameter = VSTESTCD == "HEIGHT",
  analysis_var = VSSTRESN,
  set_values_to_sds = exprs(
    PARAMCD = "HGTSDS",
    PARAM = "Height-for-age z-score"
  ),
  set_values_to_pctl = exprs(
    PARAMCD = "HGTPCTL",
    PARAM = "Height-for-age percentile"
  )
)
#> # A tibble: 232 × 39
#>    USUBJID     BRTHDTC  SEX   STUDYID DOMAIN VSSEQ VSTESTCD VSTEST VSPOS VSORRES
#>    <chr>       <chr>    <chr> <chr>   <chr>  <int> <chr>    <chr>  <chr> <chr>  
#>  1 01-701-1015 2013-01… F     CDISCP… VS         1 BMI      BMI    NA    16.577…
#>  2 01-701-1015 2013-01… F     CDISCP… VS         5 BMI      BMI    NA    16.615…
#>  3 01-701-1015 2013-01… F     CDISCP… VS         9 BMI      BMI    NA    16.697…
#>  4 01-701-1015 2013-01… F     CDISCP… VS        13 BMI      BMI    NA    16.816…
#>  5 01-701-1015 2013-01… F     CDISCP… VS        17 BMI      BMI    NA    16.824…
#>  6 01-701-1015 2013-01… F     CDISCP… VS        21 BMI      BMI    NA    16.915…
#>  7 01-701-1015 2013-01… F     CDISCP… VS        25 BMI      BMI    NA    17.051…
#>  8 01-701-1015 2013-01… F     CDISCP… VS        29 BMI      BMI    NA    17.162…
#>  9 01-701-1015 2013-01… F     CDISCP… VS        33 BMI      BMI    NA    17.248…
#> 10 01-701-1015 2013-01… F     CDISCP… VS        37 BMI      BMI    NA    17.433…
#> # ℹ 222 more rows
#> # ℹ 29 more variables: VSORRESU <chr>, VSSTRESC <chr>, VSSTRESN <dbl>,
#> #   VSSTRESU <chr>, VSSTAT <chr>, VSLOC <chr>, VSBLFL <chr>, VISITNUM <dbl>,
#> #   VISIT <chr>, VISITDY <int>, VSDTC <chr>, VSDY <int>, VSTPT <chr>,
#> #   VSTPTNUM <dbl>, VSELTM <chr>, VSTPTREF <chr>, VSEVAL <chr>, EPOCH <chr>,
#> #   VSDT <date>, BRTHDT <date>, AGECUR_D <dbl>, CURU_D <chr>, AGECUR_M <dbl>,
#> #   CURU_M <chr>, AGECUR <dbl>, AGECURU <chr>, AVAL <dbl>, PARAMCD <chr>, …