Imputation of Missing Continuous Glucose Monitor Data

Abstract

Background:

Continuous glucose monitors (CGMs) in research and clinical settings characterize glycemic profiles through repeated measurement of interstitial glucose levels on the order of minutes. Missing values from devices are unavoidable. Data from the Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs) study were used to investigate the impact of missing data on CGM summary metrics. Several imputation techniques were evaluated by comparing mean relative bias (MRB) between true and imputed CGM data for the summary metrics.

Methods:

We used 105 CGM profiles with nine days of complete glucose measurements and introduced missing data strings using a zero-inflated negative binomial hurdle model. Overall missingness was introduced at 2% consistent with GO MOMs data and increased to 5%, 10%, and 20%. Imputation approaches included single, multiple, machine learning techniques, and hot-deck imputation, where missing values are replaced with the participant’s observed values. Removing missing values prior to analysis (complete case analysis) was also evaluated.

Results:

The MRB is minimal across most metrics and imputation methods at overall 2% missing data and increases with higher missing data frequency, with trends depending on metric and imputation method. Hot-deck imputation and complete case analysis show consistently low MRB.

Conclusions:

Missing CGM data are to be expected. For periods of wear with up to 20% missing data, hot-deck imputation and complete case analysis may be acceptable if data are missing completely at random. Explored imputation techniques are robust, but each has their own limitations, which should be considered if these techniques are implemented.