Statistical techniques used in analysing simultaneous continuous glucose monitoring and ambulatory electrocardiography in patients with diabetes: A systematic review.

Abstract

ObjectivesThere has been a steady increase in the number of studies of the complex relationship between glucose and electrical cardiac activity which use simultaneous continuous glucose monitors (CGM) and continuous electrocardiogram (ECG). However, data collected on the same individual tend to be similar (yielding correlated or dependent data) and require analyses that take into account that correlation. Many opt for simplified techniques such as calculating one measure from the data collected and analyse one observation per subject. These simplified methods may yield inconsistent and biased results in some instances. In this systematic review, we aim to examine the adequacy of the statistical analyses performed in such studies and make recommendations for future studies.Research questionsWhat are the objectives of studies collecting simultaneous CGM and ECG data? Do methods used in analysing CGM and continuous ECG data fully optimise the data collected?DesignSystematic review.Data sourcesPubMed and Web of Science.MethodsA comprehensive search of the PubMed and Web of Science databases to June 2022 was performed. Studies utilising CGM and continuous ECG simultaneously in people with diabetes were included. We extracted information about study objectives, technologies used to collect data and statistical analysis methods used for analysis. Reporting was done following PRISMA guidelines.ResultsOut of 118 publications screened, a total of 31 studies met the inclusion criteria. There was a diverse array of study objectives, with only two studies exploring the same exposure-outcome relationship, allowing only qualitative analysis. Only seven studies (23%) incorporated methods which fully utilised the study data using methods that yield the correct power and minimize type I error rate. The rest (77%) used analyses that summarise the data first before analysis and/or totally ignored data dependency. Of those who applied more advanced methods, one study performed both simple and correct analyses and found that ignoring data structure resulted in no association whilst controlling for repeated measures yielded a significant relationship.ConclusionMost studies underutilised statistical methods suitable for analysis of dynamic continuous data, potentially attenuating their statistical power and overall conclusions. We recommend that aggregated data be used only as exploratory analysis, while primary analysis should use methods applied to the raw data such as mixed models or functional data analyses. These methods are widely available in many free, open source software applications.