sd_measures {iglu}

Calculate SD subtypes

Description

The function sd_measures produces SD subtype values in a tibble object with a row for each subject and columns corresponding to id followed by each SD subtype.

Usage

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

Arguments

data

DataFrame object with column names "id", "time", and "gl".

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 SD subtype values is returned.

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

1. SDw - vertical within days:

   Calculated by first taking the standard deviation of each day's glucose measurements, then taking the mean of all the standard deviations. That is, for d days we compute SD_1 ... SD_d daily standard deviations and calculate 1/d * \sum [(SD_i)]

2. SDhhmm - between time points:

   Also known as SDhh:mm. Calculated by taking the mean glucose values at each time point in the grid across days, and taking the standard deviation of those mans. That is, for t time points we compute X_t means for each time point and then compute SD([X_1, X_2, ... X_t]).

3. SDwsh - within series:

   Also known as SDws h. Calculated by taking the hour-long intervals starting at every point in the interpolated grid, computing the standard deviation of the points in each hour-long interval, and then finding the mean of those standard deviations. That is, for n time points compute SD_1 ... SD_n, where SD_i is the standard deviation of the glucose values [X_i, X_{i+1}, ... X_{i+k}] corresponding to hour-long window starting at observation X_i, the number of observations in the window k depends on CGM meter frequency. Then, take 1/n * \sum [(SD_i)].

4. SDdm - horizontal sd:

   Calculated by taking the daily mean glucose values, and then taking the standard deviation of those daily means. That is, for d days we take X_1 ... X_d daily means, and then compute SD([X_1, X_2, ... X_d]).

5. SDb - between days, within timepoints:

   Calculated by taking the standard deviation of the glucose values across days for each time point, and then taking the mean of those standard deviations. That is, for t time points take SD_1 ... SD_t standard deviations, and then compute 1/t * \sum[(SD_i)]

6. SDbdm - between days, within timepoints, corrected for changes in daily means:

   Also known as SDb // dm. Calculated by subtracting the daily mean from each glucose value, then taking the standard deviation of the corrected glucose values across days for each time point, and then taking the mean of those standard deviations. That is, for t time points take SD_1 ... SD_t standard deviations, and then compute 1/t * \sum[(SD_i)]. where SD_i is the standard deviation of d daily values at the 1st time point, where each value is the dth measurement for the ith time point subtracted by the mean of all glucose values for day d.

Value

A tibble object with a column for id and a column for each of the six SD subtypes.

References

Rodbard (2009) New and Improved Methods to Characterize Glycemic Variability Using Continuous Glucose Monitoring Diabetes Technology and Therapeutics 11 .551-565, doi:10.1089/dia.2009.0015.

Examples

data(example_data_1_subject)
sd_measures(example_data_1_subject)