glucopy.metrics.mard#
- glucopy.metrics.mard(cgm_df: DataFrame, smbg_df: DataFrame, slack: int = 0, interpolate: bool = True)[source]#
Calculates the Mean Absolute Relative Difference (MARD).
\[MARD = \frac{1}{N} \sum_{i=1}^N \frac{|CGM_i - SMBG_i|}{SMBG_i} * 100\]\(N\) is the number of SMBG readings.
\(CGM_i\) is the Continuous Glucose Monitoring (CGM) value at time i.
\(SMBG_i\) is the Self-Monitoring of Blood Glucose (SMBG) value at time i.
- Parameters:
cgm_df (pandas.DataFrame) – DataFrame containing the CGM values. The dataframe must contain ‘CGM’ and ‘Timestamp’ columns present in
glucopy.Gframe.data.smbg_df (pandas.DataFrame) – DataFrame containing the SMBG values. The dataframe must contain ‘SMBG’ and ‘Timestamp’ columns present in
glucopy.Gframe.data.slack (int, default 0) – Maximum number of minutes that a given CGM value can be from an SMBG value and still be considered a match.
interpolate (bool, default True) – If True, the SMBG values will be interpolated to the CGM timestamps. If False, Only CGM values that have corresponding SMBG values will be used.
- Returns:
mard – Mean Absolute Relative Difference (MARD).
- Return type:
float
Notes
This function is meant to be used by
glucopy.Gframe.mard()