How to accurately identify factors of cancer occurrence and to provide intervention early are the key issues that urgently need to be addressed in cancer prevention and treatment. Mendelian randomization (MR) analysis uses genetic variants as instrument variables for exposures of interest, which compensates the shortcomings of traditional observational studies and clinical trials. This review introduced the current application status of MR analysis in cancer etiology and treatment researches in details, including assessment of cancer risk factors, exploration of cancer treatment targets, and evaluation of drug efficiency and adverse reactions. The scopes and dimensions of cancer etiology and treatment researches are greatly expanded because of various MR designs and abundant high-level omics data. As well, it provides a practical and feasible method for constructing cancer etiology networks and drug targeted databases, which are promising for supporting the development of precision cancer prevention and treatment.
Incidence rate is a common effect measure. The incidence rate ratio refers to the ratio of two different incidence rates. It is used to compare the difference in the number of cases per unit person-time between two groups. RevMan software can not perform a meta-analysis with the incidence rate ratio as the effect size at present. A set of simulation data was used to demonstrate a meta-analysis process with the incidence rate ratio as the effect size by using the meta package of R Studio software in this article.
By comparing the diagnostic accuracy of two or more tests in the same study, the one with the higher diagnostic accuracy can be screened. Therefore, it is extremely important to conduct the comparative diagnostic test accuracy study. This paper introduced the concept of the comparative diagnostic test accuracy study, compared it with single diagnostic test accuracy study, and described its role, study design, statistical analysis, current status, and challenges.
The correct and reasonable statistical analysis method can make the results of comparative diagnosis test accuracy more convincing. In this paper, the accuracy of diagnostic tests is divided into 2 forms: binary-scale outcomes and ordinal-scale/continuous-scale outcomes. Taking diagnostic indicators such as sensitivity, specificity, receiver operating characteristic (ROC) curves and area under curve (AUC) values as entry points, combined with examples, this paper introduced how to compare the diagnostic results of tests by parameter estimation and hypothesis testing, with the aim of providing references for the comparative diagnosis test accuracy.