Most statistical data in observational studies is expressed as the effect value and its 95% confidence interval (95% CI), which do not correspond to the data format used for traditional meta-analyses, so special data conversion is to be needed when Review Manager software is applied to do a meta-analysis for this type of data, which will make the operation complicated and cumbersome. In addition, Stata software is such a powerful statistical software that can be used directly to conduct a meta-analysis with the effect value and its 95% CI. Therefore, it is an indispensable statistical tool for meta-analysis in observational studies. And this study will give a brief introduction how to use Stata software to conduct a meta-analysis with effect value and its 95% CI based on the published meta-analysis data.
Objective When making causal inferences in observational studies, in order to improve the robustness of the results of observational studies, statistical analysis techniques are often used to estimate the impact of unmeasured potential confounding factors. By systematically reviewing the application progress of the E-value, one of the sensitivity analysis methods, the advantages and limitations of using the E-value were discussed, to provide references for the application, reporting and interpretation of the E-value. Methods In the PubMed database, E-value was used as a keyword for title, abstract and key paper citation retrieval, and the literature that used the E-value analysis method for sensitivity analysis during 2016-2021 was screened. Results The E-value was widely used not only in cohort studies (n=215) and case-control studies (n=15), but also in cross-sectional studies (n=28), randomized controlled trials (n=6) and meta-analysis (n=16). The E-value was often combined with other sensitivity analysis methods, such as hierarchical analysis, instrumental variables, and multiple statistical regression models that correct different covariates, to further explore the reliability and robustness of the results. Conclusion When the E-value is used to evaluate the confounding factors in observational studies, the confidence interval and P value can be combined to evaluate the sensitivity of the results more comprehensively.