cv_measures {iglu}

Calculate Coefficient of Variation subtypes

Description

The function cv_measures produces CV subtype values in a tibble object.

Usage

cv_measures(data, dt0 = NULL, inter_gap = 45, tz = "" )

Arguments

data

DataFrame object with column names "id", "time", and "gl". Should only be data for 1 subject. In case multiple subject ids are detected, a warning is produced and only 1st subject is used.

dt0

The time frequency for interpolation in minutes, the default will match the CGM meter's frequency (e.g. 5 min for Dexcom).

inter_gap

The maximum allowable gap (in minutes) for interpolation. The values will not be interpolated between the glucose measurements that are more than inter_gap minutes apart. The default value is 45 min.

tz

A character string specifying the time zone to be used. System-specific (see as.POSIXct), but " " is the current time zone, and "GMT" is UTC (Universal Time, Coordinated). Invalid values are most commonly treated as UTC, on some platforms with a warning.

Details

A tibble object with 1 row for each subject, a column for subject id and a column for each cv subtype values is returned.

Missing values will be linearly interpolated when close enough to non-missing values.

1. CVmean:

   Calculated by first taking the coefficient of variation of each day's glucose measurements, then taking the mean of all the coefficient of variation. That is, for x days we compute cv_1 ... cv_x daily coefficient of variations and calculate 1/x * \sum [(cv_i)]

2. CVsd:

   Calculated by first taking the coefficient of variation of each day's glucose measurements, then taking the standard deviation of all the coefficient of variations. That is, for d days we compute cv_1 ... cv_d daily coefficient of variations and calculate SD([cv_1, cv_2, ... cv_d])

Value

When a data.frame object is passed, then a tibble object with three columns: subject id and corresponding CV subtype values is returned.

References

Umpierrez, et.al. (2018) Glycemic Variability: How to Measure and Its Clinical Implication for Type 2 Diabetes The American Journal of Medical Sciences 356 .518-527, doi:10.1016/j.amjms.2018.09.010.

Examples

data(example_data_1_subject)
cv_measures(example_data_1_subject)

data(example_data_5_subject)
cv_measures(example_data_5_subject)