Impact of Missing Data and Monitoring Duration on Downstream Analyses in Continuous Glucose Monitoring

Consensus guidelines recommend at least 14 consecutive days of continuous glucose monitoring (CGM) with 70% completeness to represent 90-day glycemic exposure. This study quantifies bias and uncertainty introduced into downstream analyses by using CGM metrics from incomplete or reduced monitoring, relative to a 90-day complete profile.

Using a type 1 diabetes cohort with 1,010 complete 90-day CGM profiles, we simulated incomplete profiles by varying monitoring duration and data completeness. Consensus CGM metrics were computed on incomplete and complete profiles to quantify measurement error, which was propagated into two downstream regression models: 1) CGM metric is an outcome for a binary treatment (clinical trial setting); 2) CGM metric is an explanatory variable (covariate) for another continuous outcome. Bias was quantified using observed-to-true effect size ratios and uncertainty by the sample size increase required to maintain precision.

In the clinical trial setting, treatment effects remain unbiased but lose precision; for time in range (TIR), 14 days required ≥16% more participants versus 90 days; 30 days required ≥6.5%. When the CGM metric is a covariate, associations with outcomes are attenuated (biased toward zero up to 14% at 14 days and 6% at 30 days for TIR) and less precise.

Representing 90 days of glycemic exposure with 14 days can lead to bias and loss of precision in downstream analyses. We recommend study protocols require at least 30 days of CGM with 70% completeness. If 30 days is not feasible, studies should plan for increased sample sizes.