Hypothesis Testing for Matched Pairs with Missing Data by Maximum Mean Discrepancy: An Application to Continuous Glucose Monitoring

Matabuena, Marcos; Félix, Paulo; Ditzhaus, Marc; Vidal, Juan C.; Gudé, Francisco

doi:10.1080/00031305.2023.2200512

Cited by 2 publications

(2 citation statements)

References 59 publications

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“…This technique belongs to a family of statistical tests specifically tailored for this purpose. A similar methodology can be found in our previous work, Matabuena et al 15 This approach is consistent with the methodology used and explained in the original glucodensity paper for testing statistical differences between two populations. 10…”

Section: Outcomes and Statistical Analysissupporting

confidence: 64%

Insulin glargine 300 U/ml versus insulin degludec 100 U/ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities‐based post hoc analysis of the OneCare study

Gomez‐Peralta,

Chico Ballesteros,

Marco Martínez

et al. 2024

Diabetes Obesity Metabolism

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Section: Outcomes and Statistical Analysissupporting

confidence: 64%

Insulin glargine 300 U/ml versus insulin degludec 100 U/ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities‐based post hoc analysis of the OneCare study

Gomez‐Peralta,

Chico Ballesteros,

Marco Martínez

et al. 2024

Diabetes Obesity Metabolism

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“…( 13 ) noted that glucodensity can be used “to establish if there are statistically significant differences between patients subjected to different interventions, for example, in a clinical trial.” Furthermore, Matabuena et al. ( 14 , 15 , 43 ) proposed methods and estimators for handling missing data and exploring their potential values in analyzing CGM data. In our considered setting, the main objects of interest are probability density functions, and functional data analysis techniques may be useful for statistical inference.…”

Section: Discussionmentioning

confidence: 99%

Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis

Cui,

Goldfine,

Quinlan

et al. 2023

Front. Clin. Diabetes Healthc.

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IntroductionContinuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques.MethodsIn this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta-cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range.ResultsDistinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range.DiscussionOur findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of--and provides opportunities for--taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data.

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Hypothesis Testing for Matched Pairs with Missing Data by Maximum Mean Discrepancy: An Application to Continuous Glucose Monitoring

Cited by 2 publications

References 59 publications

Insulin glargine 300 U/ml versus insulin degludec 100 U/ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities‐based post hoc analysis of the OneCare study

Insulin glargine 300 U/ml versus insulin degludec 100 U/ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities‐based post hoc analysis of the OneCare study

Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis

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