To perform a meta-analysis of single nucleotide polymorphism needs to calculate gene frequency. This paper employs allele model as an example to introduce how to calculate gene frequency and display the process of a meta-analysis of single nucleotide polymorphism data using Review Manager 5.3 software.
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.
Survival data were widely used in oncology clinical trials. The methods used, such as the log-rank test and Cox regression model, should meet the assumption of proportional hazards. However, the survival data with non-proportional hazard (NPH) are also quite usual, which will decrease the power of these methods and conceal the true treatment effect. Therefore, during the trial design, we need to test the proportional hazard assumption and plan different analysis methods for different testing results. This paper introduces some methods that are widely used for proportional hazard testing, and summarizes the application condition, advantages and disadvantages of analysis methods for non-proportional hazard survival data. When the non-proportional hazard occurs, we need to choose the suitable method case by case and to be cautious in the interpretation of the results.