clean_htwt: Height and Weight anomaly detection.
In jadonwagstaff/pkpd:

Description Usage Arguments Details Value Examples

Uses CDC height and weight expected values to determine anomalies in height and weight values for cleaning purposes.

1 2	clean_htwt(df, HEIGHT = HEIGHT, WEIGHT = WEIGHT, SEX = SEX, TIME = TIME, BIRTH_DT = BIRTH_DT, AGE_M = AGE_M)

`df`	Data frame containing weight, height, the time weight and height were collected, sex, and eighter birth date or age in months.
`HEIGHT`	Column name for patient height (cm).
`WEIGHT`	Column name for patient weight (kg).
`SEX`	Column name for patient sex 0 = female, 1 = male.
`TIME`	Column name for dates when corresponding height and weight values were collected. Must be R datetime object.
`BIRTH_DT`	Column name for birth date of patient. Must be R date or datetime object.
`AGE_M`	Column name for age of patient in months.

See https://www.cdc.gov/growthcharts/percentile_data_files.htm for data sources used to detect anomalies. Z-scores are calculated based on LMS parameters as described in [1]. If subject is greater than 20 years old, height and weight will be compared to the distribution for a 20 year old.

If any dataframe input column has a different name, define that columns name in the input parameters (See Examples). See http://r4ds.had.co.nz chapters 11.4, 11.5 and 16 for creating date or datetime R objects.

1. Flegal KM, Cole TJ. Construction of LMS parameters for the Centers for Disease Control and prevention 2000 growth chart. National health statistics reports; no 63. Hyattsville, MD: National Center for Health Statistics. 2013.

Returns a data frame identifying height weight anomalies with specific colums:

HT_Z: Z score on expected distribution of height for age and sex.

WT_Z: Z score on expected distribution of Weight for age and sex.

htwt <- clean_htwt(htwt_example, HEIGHT = HT, WEIGHT = WT)
htwt
# filter out results for outside of 5th and 95th percentile
htwt <- dplyr::filter(htwt, abs(HT_Z) > 1.96 | abs(WT_Z) > 1.96)
htwt