cv_measures: Calculate Coefficient of Variation subtypes

In stevebroll/iglu: Interpreting Glucose Data from Continuous Glucose Monitors

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, the 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 * ∑ [(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)

stevebroll/iglu documentation built on Jan. 30, 2021, 1:53 a.m.