The function roc produces rate of change values in a tibble object.
Arguments
- data
DataFrame object with column names "id", "time", and "gl".
- timelag
Integer indicating the time period (# minutes) over which rate of change is calculated. Default is 15, e.g. rate of change is the change in glucose over the past 15 minutes divided by 15.
- 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 a column for subject id and a column for ROC values is
returned. A ROC value is returned for each time point for all the subjects. Thus
multiple rows are returned for each subject. If the rate of change cannot be
calculated, the function will return NA for that point.
The glucose values are linearly interpolated over a time grid starting at the
beginning of the first day of data and ending on the last day of data. Because
of this, there may be many NAs at the beginning and the end of the roc values
for each subject. These NAs are a result of interpolated time points that do
not have recorded glucose values near them because recording had either not
yet begun for the day or had already ended.
The ROC is calculated as \(\frac{G(t_i) - G(t_{i-1})}{t_i - t_{i-1}}\) where \(G_i\) is the Glucose measurement at time \(t_i\) and \(G_{i-1}\) is the Glucose measurement at time \(t_{i-1}\). The time difference between the points, \(t_i - t_{i-1}\), is selectable and set at a default of 15 minutes.
References
Clarke et al. (2009) Statistical Tools to Analyze Continuous Glucose Monitor Data, Diabetes Diabetes Technology and Therapeutics 11 S45-S54, doi:10.1089/dia.2008.0138 .
Examples
data(example_data_1_subject)
roc(example_data_1_subject)
#> # A tibble: 4,032 × 2
#> id roc
#> <fct> <dbl>
#> 1 Subject 1 NA
#> 2 Subject 1 NA
#> 3 Subject 1 NA
#> 4 Subject 1 NA
#> 5 Subject 1 NA
#> 6 Subject 1 NA
#> 7 Subject 1 NA
#> 8 Subject 1 NA
#> 9 Subject 1 NA
#> 10 Subject 1 NA
#> # ℹ 4,022 more rows
roc(example_data_1_subject, timelag = 10)
#> # A tibble: 4,032 × 2
#> id roc
#> <fct> <dbl>
#> 1 Subject 1 NA
#> 2 Subject 1 NA
#> 3 Subject 1 NA
#> 4 Subject 1 NA
#> 5 Subject 1 NA
#> 6 Subject 1 NA
#> 7 Subject 1 NA
#> 8 Subject 1 NA
#> 9 Subject 1 NA
#> 10 Subject 1 NA
#> # ℹ 4,022 more rows
data(example_data_5_subject)
roc(example_data_5_subject)
#> # A tibble: 18,720 × 2
#> id roc
#> <fct> <dbl>
#> 1 Subject 1 NA
#> 2 Subject 1 NA
#> 3 Subject 1 NA
#> 4 Subject 1 NA
#> 5 Subject 1 NA
#> 6 Subject 1 NA
#> 7 Subject 1 NA
#> 8 Subject 1 NA
#> 9 Subject 1 NA
#> 10 Subject 1 NA
#> # ℹ 18,710 more rows