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

• Main Authors: Deepthy M.S (Author), Harichandrakumar K.T (Author), Sreejith Parameswaran (Author), Tamilarasu Kadhiravan (Author), N. Sreekumaran Nair (Author)
• Format: Book
• Published: Elsevier, 2022-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2213-3984; 10.1016/j.cegh.2022.101029
• Keywords: Multivariate meta-analysis; Multiple correlated outcomes; Joint synthesis; Within-study correlation; Sensitivity analysis; Public aspects of medicine; RA1-1270
• Source: Clinical Epidemiology and Global Health, Vol 15, Iss , Pp 101029- (2022)

Background: 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. Methods: 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. Results: 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. Conclusions: 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.