Evidence map›Paper›PMID 41911328›Full record

ArticleDiabetes care2026

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

Neo Kok, Walter T Williamson, Joyce M Lee, Irina Gaynanova

Abstract read
In one paragraph

Article in Diabetes care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Neo KokDepartment of Biostatistics, University of Michigan, Ann Arbor, MI.
Walter T WilliamsonDepartment of Biostatistics, University of Michigan, Ann Arbor, MI.
Joyce M LeeSusan B. Meister Child Health Evaluation and Research Center, Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI.ORCID 0000-0002-8147-5168
Irina GaynanovaDepartment of Biostatistics, University of Michigan, Ann Arbor, MI.ORCID 0000-0002-4116-0268

Funding

Regional Pilot And Feasibility Study Grants ProgramP30DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Mehboob A Hussain · 2013 to 2026
$24.3M
Pilot and Feasibility (P and F) ProgramP30DK089503 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Kanakadurga Singer · 2010 to 2026
$20.3M
Research BaseP30DK092926 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MARY ELLEN MICHELE HEISLER, ADESUWA B OLOMU · 2011 to 2026
$10.0M
New machine learning methods for extracting features from digital health data with applications to sleep apneaR01HL172785 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Irina Gaynanova · 2024 to 2026
$1.1M
NHLBI NIH HHS R01 HL172785NIDDK NIH HHS P30 DK020572NIDDK NIH HHS P30 DK089503NIH HHS P30DK020572NIH HHS P30DK089503NIH HHS P30DK092926NIH HHS R01HL172785
6 · The paper itself

Abstract

objectiveConsensus 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. RESEARCH DESIGN AND

methodsUsing 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.

resultsIn 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.

conclusionsRepresenting 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.

Indexed as

Blood GlucoseContinuous Glucose MonitoringDiabetes Mellitus, Type 1Blood Glucose Self-MonitoringHumansBlood Glucose

Identifiers

PMID41911328
PMCPMC13191399

What OpenQuestion holds

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Registered trials

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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.