The averages and standard deviations of several subgroups in a study are sometimes need to be combined in meta-analysis, the calculating method to do this was given in this paper. Firstly, a commonly used incorrect calculating method to do the combination was pointed out; it origins from metrology and should be applied in the combination of standard deviations of several groups of measurements of the same object, and it's not fit for the combination of several subgroups' data in meta-analysis. Meanwhile the calculating error of this method was tested with real and constructed data and its impact on the conclusion of meta-analysis was analyzed. The incorrect method may result in a big calculating error with the real research data and even lead to an incorrect conclusion with the constructed data. The correct formula was deduced and given, the calculating result of the correct formula equals the true value when neglecting the round error. The error analysis of the incorrect method was then done with the correct formula and an accurate combination result can only be got when meeting the condition given by this paper. The correct method in this paper should be used to combine standard deviations in the future researches.
The paper presents two statistical methods to compare summary estimates of different subgroups in meta-analysis. It also shows how to use Z test and meta-regression model with dichotomous data and continuous data in R software to explain the similarities and differences between the two statistical methods by examples.
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.
Subpopulation treatment effect pattern plot (STEPP) method is a method for examining the relationship between treatment effects and continuous covariates and is characterized by dividing the study population into multiple overlapping subpopulations to be analyzed based on continuous covariate values. STEPP method has a different purpose than traditional subgroup analyses, and STEPP has a clear advantage in exploring the relationship between treatment effects and continuous covariates. In this study, the concepts, advantages, and subpopulation delineation methods of the STEPP method are introduced, and the specific operation process and result interpretation methods of STEPP method analysis using the STEPP package in R language are presented with examples.