The function cv_measures produces CV subtype values in a tibble object.
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.
Value
When a data.frame object is passed, then a tibble object with three columns: subject id and corresponding CV subtype values is returned.
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.
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 \(1/x * \sum [(cv_i)]\)
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])
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)
#> # A tibble: 1 × 3
#> id CVmean CVsd
#> <fct> <dbl> <dbl>
#> 1 Subject 1 21.1 7.80
data(example_data_5_subject)
cv_measures(example_data_5_subject)
#> # A tibble: 5 × 3
#> id CVmean CVsd
#> <fct> <dbl> <dbl>
#> 1 Subject 1 21.1 7.80
#> 2 Subject 2 17.0 6.41
#> 3 Subject 3 27.1 9.40
#> 4 Subject 4 18.4 5.70
#> 5 Subject 5 29.0 7.56