ArticleJournal of diabetes science and technology2026
Imputation of Missing Continuous Glucose Monitor Data.
Article in Journal of diabetes science and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Early Pregnancy Glycemic Testing for Prediction of Gestational Diabetes Mellitus and Large-for-Gestational-Age Birth Weight.Diabetes care · 2026Observational
- Summarizing data from continuous glucose monitors using the cgmstats command.The Stata journal · 2026Article
- Summarizing data from continuous glucose monitors using the cgmstats package.medRxiv : the preprint server for health sciences · 2026Article
- Beyond Time in Range: Hidden Statistical Challenges of Continuous Glucose Monitoring Data in Diabetes Drug Development.Journal of diabetes science and technology · 2026Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundContinuous 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.
methodsWe 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.
resultsThe 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.
conclusionsMissing 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.