Comparison of three meta-analytic methods using data from digital interventions on type 2 diabetes
Received 13 July 2018
Accepted for publication 9 November 2018
Published 19 December 2018 Volume 2019:12 Pages 59—73
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 4
Editor who approved publication: Dr Konstantinos Tziomalos
Mihiretu M Kebede,1–3 Manuela Peters,1,2 Thomas L Heise,1,2 Claudia R Pischke2
1Department of Public Health, University of Bremen, Health Sciences, Bremen, Germany; 2Department of Prevention and Evaluation, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany; 3Department of Health Informatics, University of Gondar, College of Medicine and Health Science, Institute of Public Health, Gondar, Ethiopia
Aims: Pooling the effect sizes of randomized controlled trials (RCTs) from continuous outcomes, such as glycated hemoglobin level (HbA1c), is an important method in evidence syntheses. However, due to challenges related to baseline imbalances and pre/post correlations, simple analysis of change scores (SACS) and simple analysis of final values (SAFV) meta-analyses result in under- or overestimation of effect estimates. This study was aimed to compare pooled effect sizes estimated by Analysis of Covariance (ANCOVA), SACS, and SAFV meta-analyses, using the example of RCTs of digital interventions with HbA1c as the main outcome.
Materials and methods: Three databases were systematically searched for RCTs published from 1993 through June 2017. Two reviewers independently assessed titles and abstracts using predefined eligibility criteria, assessed study quality, and extracted data, with disagreements resolved by arbitration from a third reviewer.
Results: ANCOVA, SACS, and SAFV resulted in pooled HbA1c mean differences of –0.39% (95% CI: [–0.51, –0.26]), –0.39% (95% CI: [–0.51, –0.26]), and –0.34% (95% CI: [–0.48–0.19]), respectively. Removing studies with both high baseline imbalance (≥±0.2%) and pre/post correlation of ≥±0.6 resulted in a mean difference of –0.39% (95% CI: [–0.53, –0.26]), –0.40% (95% CI: [–0.54, –0.26]), and –0.33% (95% CI: [–0.48, –0.18]) with ANCOVA, SACS, and SAFV meta-analyses, respectively. Substantial heterogeneity was noted. Egger’s test for funnel plot symmetry did not indicate evidence of publication bias for all methods.
Conclusion: By all meta-analytic methods, digital interventions appear effective in reducing HbA1c in type 2 diabetes. The effort to adjust for baseline imbalance and pre/post correlation using ANCOVA relies on the level of detail reported from individual studies. Reporting detailed summary data and, ideally, access to individual patient data of intervention trials are essential.
Keywords: baseline imbalance, ANCOVA, change scores, final values, systematic reviews, HbA1c, diabetes, eHealth
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