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

Source: R/derive_params_growth_height.R
derive_params_growth_height.Rd

Derive Anthropometric indicators (Z-Scores/Percentiles-for-Height/Length) based on Standard Growth Charts for Weight by Height/Length

Usage

derive_params_growth_height(
  dataset,
  sex,
  height,
  height_unit,
  meta_criteria,
  parameter,
  analysis_var,
  who_correction = FALSE,
  set_values_to_sds = NULL,
  set_values_to_pctl = NULL
)

Arguments

dataset

Input dataset

The variables specified in sex, height, height_unit, parameter, analysis_var are expected to be in the dataset.

sex

Sex

A character vector is expected.

Expected Values: M, F

height

Current Height/length

A numeric vector is expected. Note that this is the actual height/length at the current visit.

height_unit

Height/Length Unit A character vector is expected.

Expected values: cm

meta_criteria

Metadata dataset

A metadata dataset with the following expected variables: HEIGHT_LENGTH, HEIGHT_LENGTHU, SEX, L, M, S

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

Datasets who_wt_for_lgth_boys/who_wt_for_lgth_girls are applicable for subject age < 730.5 days.

If the height value from dataset falls between two HEIGHT_LENGTH values in meta_criteria, then the L/M/S values that are chosen/mapped will be the HEIGHT_LENGTH that has the smaller absolute difference to the value in height. e.g. If dataset has a current age of 50.49 cm, and the metadata contains records for 50 and 51 cm, the L/M/S corresponding to the 50 cm record will be used.

  • HEIGHT_LENGTH - Height/Length

  • HEIGHT_LENGTHU - Height/Length 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 WHO metadata available for Weight available in the admiralpeds package. Weight measures are expected to be in the unit "kg".

analysis_var

Variable containing anthropometric measurement

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

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 = "WGTHSDS", PARAM = "Weight-for-height 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 = "WGTHPCTL", PARAM = "Weight-for-height 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_age()

Examples

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

# derive weight for height/length only for those under 2 years old using WHO
# weight for length reference file
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 = AAGECUR,
    new_var_unit = AAGECURU,
    start_date = BRTHDT,
    end_date = VSDT,
    out_unit = "days"
  )

heights <- vs_peds %>%
  filter(VSTESTCD == "HEIGHT") %>%
  select(USUBJID, VSSTRESN, VSSTRESU, VSDTC) %>%
  rename(
    HGTTMP = VSSTRESN,
    HGTTMPU = VSSTRESU
  )

advs <- advs %>%
  right_join(., heights, by = c("USUBJID", "VSDTC"))

advs_under2 <- advs %>%
  filter(AAGECUR < 730.5)

who_under2 <- bind_rows(
  (admiralpeds::who_wt_for_lgth_boys %>%
    mutate(
      SEX = "M",
      height_unit = "cm"
    )
  ),
  (admiralpeds::who_wt_for_lgth_girls %>%
    mutate(
      SEX = "F",
      height_unit = "cm"
    )
  )
) %>%
  rename(
    HEIGHT_LENGTH = Length,
    HEIGHT_LENGTHU = height_unit
  )

derive_params_growth_height(
  advs_under2,
  sex = SEX,
  height = HGTTMP,
  height_unit = HGTTMPU,
  meta_criteria = who_under2,
  parameter = VSTESTCD == "WEIGHT",
  analysis_var = VSSTRESN,
  who_correction = TRUE,
  set_values_to_sds = exprs(
    PARAMCD = "WGTHSDS",
    PARAM = "Weight-for-height/length z-score"
  ),
  set_values_to_pctl = exprs(
    PARAMCD = "WGTHPCTL",
    PARAM = "Weight-for-height/length percentile"
  )
)
#> # A tibble: 162 × 37
#>    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 152 more rows
#> # ℹ 27 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>, AAGECUR <dbl>, AAGECURU <chr>, HGTTMP <dbl>,
#> #   HGTTMPU <chr>, AVAL <dbl>, PARAMCD <chr>, PARAM <chr>