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 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.
Objective To analyze the causal relationship between educational attainment and the risk of systemic lupus erythematosus (SLE). Methods Based on the data from publicly available genome-wide association studies, we employed single nucleotide polymorphisms (SNPs) strongly associated with educational attainment as instrumental variables. Two-sample Mendelian randomization analysis was used to investigate the causal relationship between educational attainment and SLE. The primary analysis method used was the inverse variance weighted with multiplicative random effects. Validation methods included inverse variance weighted with fixed effects and MR-Egger methods. Additionally, sensitivity analysis was conducted using the leave-one-out approach. Results Finally, 433 SNPs were included. The inverse variance weighted with multiplicative random effects analysis indicated no causal effect of educational attainment on the risk of SLE [odds ratio =1.111, 95% confidence interval (0.813, 1.518), P=0.509]. Similarly, the other two methods did not find any evidence of a causal relationship (P>0.05); however, significant heterogeneity was observed. The MR-Egger regression analysis provided no evidence of horizontal pleiotropy among the included instrumental variables (P>0.05). The leave-one-out approach did not identify any individual SNP that had a significant impact on the overall effect estimate. ConclusionIn conclusion, this study does not support a causal effect of educational attainment on the risk of SLE.
The rapid advancement of causal inference is driving a paradigm shift across various disciplines. "Target trial emulation" has emerged as an exceptionally promising framework for observational real-world studies, attracting substantial attention from medical scholars and regulatory agencies worldwide. This article aims to provide an introduction to CERBOT, an online tool that assists in implementing target trial emulation studies, while highlighting the advancements in this domain. Additionally, the article provides an illustrative example to elucidate the operational process of CERBOT. The objectives are to support domestic researchers in conducting target trial emulation studies and enhance the quality of real-world studies in the domestic medical field, as well as improve the medical service level in clinical practice.
ObjectiveTo investigate whether there is a causal relationship between the intake of milk or coffee and the risk of non-alcoholic fatty liver disease (NAFLD). MethodsUsing a two-sample Mendelian randomization approach, single nucleotide polymorphisms (SNPs) associated with milk or coffee intake were used as instrumental variables, and genome-wide association study data on NAFLD were used as the outcome event. Inverse-variance weighted (IVW) and MR-Egger methods were employed to investigate the causal effect of milk or coffee intake on the risk of NAFLD. ResultsBoth analyses indicated no causal association between milk or coffee intake and the risk of NAFLD (P>0.05). Sensitivity analysis indicated the robustness of the main findings, with no outliers, heterogeneity, horizontal pleiotropy, or significant influence of individual SNPs. ConclusionThis study does not support a causal relationship between the intake of milk or coffee and the risk of NAFLD.
ObjectiveThis study applied Mendelian randomization to explore the potential causal relationship between inflammatory factors and diabetic nephropathy. MethodsSummary-level data from genome-wide association studies of inflammatory factors and diabetic nephropathy were used, and inverse variance weighted analysis was used as the primary analytical method, complemented by results from weighted median, MR-Egger regression, simple model, and median model approaches. Sensitivity analysis was used to test the reliability of the MR analysis results. ResultsIn the inverse variance weighted method, stem cell factor (OR=1.28, 95%CI 1.04 to 1.58, P=0.020) and interferon-γ (OR=1.36, 95%CI 1.10 to 1.70, P=0.005) were positively correlated with diabetic nephropathy, and diabetic nephropathy was positively correlated with interferon-inducible protein 10 (OR=0.90, 95%CI 0.83 to 0.98, P=0.012) were negatively correlated with diabetic nephropathy. Sensitivity analysis showed that MR analysis was reliable. ConclusionStem cell factors and interferon-γ are associated with an increased risk of developing diabetic nephropathy, and diabetic nephropathy decreases the expression of interferon-inducible protein 10 in vivo. Our results demonstrate a potential causal relationship between inflammatory factors and the development of diabetic nephropathy. This finding is of clinical significance for the pre-diagnosis and treatment of diabetic nephropathy.
ObjectiveTo investigate the potential causal relationship between four types of reproductive behaviors and rheumatoid arthritis (RA), with the goal of establishing a theoretical foundation for clinical prevention and treatment strategies. MethodsPooled gene-wide association study (GWAS) data were obtained from large publicly searchable databases. Four characteristics like menarche, menopause, the age of first pregnancy and the age of last pregnancy, which related to reproductive behavior were selected as the exposure factors and RA as the outcome factors. Single nucleotide polymorphisms (SNPs), which were strongly correlated with the phenotype of the exposure factors, were screened as the instrumental variables, and two-sample Mendelian randomization analyses were used to assess the potential causal relationship between the exposure and the disease. Results① The Mendelian randomization analysis utilizing the inverse variance weighted method on two distinct samples revealed a significant negative correlation between the age of first pregnancy and last pregnancy with the risk of RA (OR=0.91, 95%CI 0.85 to 0.98, P=0.011; OR=0.54, 95%CI 0.31 to 0.93, P=0.026). Conversely, no causal relationship was observed between menarche and menopause with RA. Sensitivity analysis confirmed the robustness of the causal relationship, while MR Egger intercept analysis did not identify any potential horizontal pleiotropy (Page of first gestation -RA=0.169, Page of last gestation -RA=0.283). ② Reverse Mendelian randomization analysis revealed a significant positive causal association between RA and the age of first pregnancy, while no causal relationship was observed with the age of last pregnancy (OR=1.07, 95%CI 1.02 to 1.11, P=0.001). ③ Multivariate Mendelian randomization analysis demonstrated that both the age of first pregnancy and last pregnancy in women were inversely associated with the risk of RA (OR=0.88, 95%CI 0.80 to 0.97, P=0.010; OR=0.68, 95%CI 0.48 to 0.97, P=0.033). ④ There existed a negative correlation between the age of pregnancy in women and the risk of developing RA, suggesting a potential protective effect. ConclusionPregnancy age may have a negative causal relationship with the risk of RA, while menarche and menopause have no causal relationship with RA.