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Abstract
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.
Acknowledgments
We would like to thank our research librarian, Lara Christianson, for her support in developing the search strategy and optimizing it to each search database. We are grateful to Professor HajoZeeb, Professor Richard D Riley, Dr Jochen Wilhelm, Dr James E Pustejovsky, Professor Vanessa Didelez, and Dr Fleur Fritz for the methodological support. In addition, we are also very grateful to all corresponding authors of the individual studies for providing us with the data we requested. We disclose that the results of this study were presented as oral presentation at the 10th Biennial Joanna Briggs Institute Colloquium 2018 in Antwerp, Belgium.
Author contributions
MMK performed conceptualization, design, systematic literature search, title and abstract screening, quality assessment, data extraction, data analysis and interpretation of the data, and write-up. MP performed title and abstract screening, and quality assessment write-up. TLH and CRP performed conceptualization, extraction of the data, and critical review. All authors contributed toward data analysis, drafting and critically revising the paper, gave final approval of the version to be published and agree to be accountable for all aspects of the work.
Disclosure
The authors report no conflicts of interest in this work.