Objective To explore the differences between large and small studies in rare events meta-analysis. Methods Empirical data were collected from The Cochrane Systematic Review Database from January 2003 to May 2018. Meta-analyses with rare events, binary outcomes involving at least 5 studies, and at least 1 large study were screened. Peto and classical ORs were used to compare the magnitude, direction and P-value. Results A total of 214 meta-analyses were included. Among 214 pairs of ORs of large and small studies, 66 pairs (30.84%) were inconsistent in the direction of ORs based on Peto OR (Kappa =0.33), and 69 pairs (32.24%) were inconsistent in the direction of ORs based on classical OR. The Peto ORs resulted in smaller P-values compared to classic ORs in a substantial (83.18%) number of cases. Conclusion There are considerable differences between large and small studies in the results of meta-analysis of rare events.
Causal inference is one of the main goals of medical research. However, due to the lack of an in-depth understanding of the theory of causal inference, researchers tend to blindly use multiple statistical methods to analyse the same question to enhance the credibility of the results, which leads to problems in interpretation of the analysis results. Based on the three basic concepts of potential outcomes, causal effects, and distributive mechanisms of the causal inference counterfactual framework, this paper introduced six main target effects in causal inference and discussed their comparability to help researchers understand the principle of causal inference and correctly interpret and compare research results to avoid misleading conclusions.
Objective To evaluate the robustness of cardiovascular meta-analysis with use of fragility index. Methods By searching PubMed, EMbase, and Web of Science databases from 2018 to 2022, relevant literature on cardiovascular meta-analysis was systematically collected and the fragility indexes were calculated; Spearman correlation analysis was used to explore the relationship between fragility index and sample size, total number of events, effect size and its confidence interval width. Results A total of 212 meta-analyses from 29 articles were included, with a median fragility index of 11 (5, 25), a median sample size of 10301 (3384, 48330), and a median total number of events of 360 (129, 1309). Most meta-analyses chose relative risk as the effect measure (179/212), and chose Mantel-Haenszel method (102/212) and random effects model (153/212). The fragility index was positively correlated with the sample size (rs=0.56, P<0.05) and the total number of events (rs=0.61, P<0.05), and negatively correlated with confidence interval width of the effect size (rs=−0.52, P<0.05). No statistically significant results were obtained in the correlation between the fragility index and effect size. Conclusion The fragility indexes of cardiovascular meta-analyses published in comprehensive journals of high impact factors and professional cardiovascular journals are generally low, and therefore lack robustness. Fragility index is suggested to be reported in medical researches, assisting in explaining the P-value.