Applications and advancements of instrumental variable approach in causal inference in environmental epidemiology / 环境与职业医学

In environmental epidemiological research, extensive non-random environmental exposures and complex confounding biases pose significant challenges when attempting causal inference. In recent years, the introduction of causal inference methods into observational studies has provided a broader range of statistical tools for causal inference research in environmental epidemiology. The instrumental variable (IV) approach, as a causal inference technique for effectively controlling unmeasured confounding factors, has gradually found application in the field of environmental epidemiological research. This article reviewed the basic principles of IV and summarized the current research progress and limitations of applying IV for causal inference in environmental epidemiology. IV application in the field of environmental epidemiology is still in the initial stage. Rational use of IV and effective integration with other causal inference methods will become the focus of the development of causal inference in environmental epidemiology. The aim of this paper is to provide a methodological reference and basis for future studies involving causal inference to target population health effects of environmental exposures in China.

Constructing and searching adjustment sets based on a causal graph model / 四川精神卫生

The purpose of this paper was to introduce the basic knowledge of the causal graph model， the contents of the CAUSALGRAPH procedure and the method of constructing and searching adjustment sets based on the CAUSALGRAPH procedure in SAS/STAT. The causal graph model was the product of the combination of graph theory and probability theory. It could find all possible adjustment sets including the minimum adjustment set based on the action relationship between the variables set by the user. The contents of the CAUSALGRAPH procedure mainly included three identification criteria， two operating modes and one verification checking method. This paper analyzed the causal effect of two instances based on the CAUSALGRAPH procedure in SAS， and explained the output results.

Mendelian randomization approach, used for causal inferences / 中华流行病学杂志

Mendelian randomization (MR) approach is based on the Mendelian genetic law,which is called "Parental alleles that randomly assigned to the offspring".MR refers to the use of genetic variants to develop causal inferences from observational data,if the variant genotype isassociated with the phenotype and the variant genotype associated with the risk of disease of interest through the phenotype.Hence,the genotype can be used as Instrumental Variable (IV) to infer the causal relation between the phenotype and the risk of diseases.In recent years,MR approach is widely used in causal inference between the exposure factors and the risks of disease,along with the rapid development of statistical methods,big datasets of GWAS,epigenetics and the various "omics" techniques.This paper provides an overview of the MR strategies and addresses the related assumptions and implications,with reliability and limitations included.

Mendelian randomization approach, used for causal inferences / 中华流行病学杂志

Mendelian randomization (MR) approach is based on the Mendelian genetic law,which is called "Parental alleles that randomly assigned to the offspring".MR refers to the use of genetic variants to develop causal inferences from observational data,if the variant genotype isassociated with the phenotype and the variant genotype associated with the risk of disease of interest through the phenotype.Hence,the genotype can be used as Instrumental Variable (IV) to infer the causal relation between the phenotype and the risk of diseases.In recent years,MR approach is widely used in causal inference between the exposure factors and the risks of disease,along with the rapid development of statistical methods,big datasets of GWAS,epigenetics and the various "omics" techniques.This paper provides an overview of the MR strategies and addresses the related assumptions and implications,with reliability and limitations included.

Instrumental Variable Methods and Its Applications in Outcome Research of Traditional Chinese Medicine / 世界科学技术-中医药现代化

This paper was aimed to discuss the feasibility and attentions of application of instrumental variable (IV) methods in traditional Chinese medicine (TCM) outcome research. First, the application of IV was introduced, which included the basic principles and hypothesis, statistical model, estimator of IV and weak IV. Then, an example was given to illustrate the evaluation criteria and attentions of IV. The resultsshowed that IV method was proposed as a potential approach to the problems of confounding in statistics. But using IV methods should be based on a series of statistical hypotheses. It was concluded that the IV analysis was a method controlling confounding bias, but generally it was not chosen as the preferred analytical method. The issue of searching for valid and plausible IV seemed to be the biggest obstacle in the outcome of TCM researches.