Application of bivariate meta-analytic approach for pooling effect measures of correlated multiple outcomes in medical research

• Published in: Clinical epidemiology and global health Vol. 15; p. 101029
• Main Authors: M.S., Deepthy, K.T., Harichandrakumar, Parameswaran, Sreejith, Kadhiravan, Tamilarasu, Sreekumaran Nair, N.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2022; Elsevier
• Subjects: Joint synthesis; Multiple correlated outcomes; Multivariate meta-analysis; Sensitivity analysis; Within-study correlation; Multiple correlated outcomes; Multivariate meta-analysis; Joint synthesis; Sensitivity analysis; Within-study correlation
• ISSN: 2213-3984; 2213-3984
• DOI: 10.1016/j.cegh.2022.101029

Multivariate meta-analysis is used when multiple correlated outcomes are reported in a systematic review. This study explored the application of multivariate meta-analysis in such a context. The objectives of the present study were to compare the summary findings and decisions between univariate and bivariate meta-analyses, as well as to assess how much sensitive the results are towards the strength of the correlation between the outcome variables. A systematic review that reported two correlated outcomes, Intact parathyroid hormone levels and serum phosphate was chosen for demonstrating the applications of bivariate meta-analysis. Both univariate and bivariate meta-analyses with fixed effect and random effect models were carried out and the results were compared. A sensitivity analysis was performed for a wide spectrum of correlations from −1 to +1 to assess the impact of correlation on pooled effect estimates and its precision. Pooled effect estimates generated through bivariate meta-analysis were found to be varying when compared to those obtained through univariate meta-analysis. The confidence interval of the pooled effect estimates obtained through bivariate meta-analysis was wider than in univariate meta-analysis. Further, the value of the pooled effect estimates along with its confidence intervals also differed for varied levels of correlations. This study observed that when we have multiple correlated outcome variables to answer a single question bivariate meta-analysis could be a better approach. The magnitude of the correlation between the outcome variables also plays a vital role in meta-analysis.