The nlme package is developed based on the generalized least squares (gls) and linear mixed-effects model (lme). It can perform meta-analysis based on linear and nonlinear mixed effects models in R language. When conducting meta-analysis using nlme package in R language, the first step is to translate the data into its logarithm estimation. In this article, we introduce how to perform network meta-analysis using R language nlme package and show the core step of data translation in detail.
R language could call OpenBUGS software for performing network meta-analysis using R2OpenBUGS package, BRugs package, and rbugs package. In this paper, we introduced how to implement network meta-analysis using these three packages. The results show that the computed results are similar for the three packages; however, the rbugs package could not draw the plot, only R2OpenBUGS package could draw forest plot.
The netmeta package is specialized for implementing network meta-analysis. This package was developed based on the theories of classical frequentist under R language framework. The netmeta package overcomes some difficulties of the software and/or packages based on the theories of Bayesian, for these software and/or packages need to set prior value when conducting network meta-analysis. The netmeta package also has the advantages of simple operation process and ease to operate. Moreover, this package can calculate and present the individual matched and pooled results based on the random and fixed effect model at the same time. It also can draw forest plots. This article gives a briefly introduction to show the process to conduct network meta-analysis using netmeta package.
This study aims to predict expression of estrogen receptor (ER) in breast cancer by radiomics. Firstly, breast cancer images are segmented automatically by phase-based active contour (PBAC) method. Secondly, high-throughput features of ultrasound images are extracted and quantized. A total of 404 high-throughput features are divided into three categories, such as morphology, texture and wavelet. Then, the features are selected by R language and genetic algorithm combining minimum-redundancy-maximum-relevance (mRMR) criterion. Finally, support vector machine (SVM) and AdaBoost are used as classifiers, achieving the goal of predicting ER by breast ultrasound image. One hundred and four cases of breast cancer patients were conducted in the experiment and optimal indicator was obtained using AdaBoost. The prediction accuracy of molecular marker ER could achieve 75.96% and the highest area under the receiver operating characteristic curve (AUC) was 79.39%. According to the results of experiment, the feasibility of predicting expression of ER in breast cancer using radiomics was verified.
The R software bmeta package is a package that implements Bayesian meta-analysis and meta-regression by invoking JAGS software. The program is based on the Markov Chain Monte Carlo (MCMC) algorithm to combine various effect quantities (OR, MD and IRR) of different types of data (dichotomies, continuities and counts). The package has the advantages of fewer command function parameters, rich models, powerful drawing function, easy of understanding and mastering. In this paper, an example is presented to demonstrate the complete operation flow of bmeta package to implement bayesian meta-analysis and meta-regression.
ObjectiveTo provide method references for data visualization of multiple linear regression analysis.MethodsAfter importing data to R Studio, this paper conducted general descriptive statistics analysis, then constructed a linear model between independent variables and the target. After checking independence of observations, the normality of the target, and the linearity between variables, this paper estimated coefficients of independent variables, dealt with multicollinearity, tested significance of estimates and performed residual analysis to guarantee that the regression met its assumptions, and eventually used the fitted model for prediction.ResultsThe multiple linear regression analysis implemented by R Studio software had better visualization functions and easier operation than traditional R language software.ConclusionsR Studio software has good application value in realizing multiple linear regression analysis data visualization.
With the increase in the number of single-arm clinical trials and lack of head-to-head clinical studies, the application of unadjusted indirect comparisons and network meta-analysis methods has been limited. Matching-adjusted indirect comparison (MAIC) is an alternative method to fully utilize individual patient data from one study and balance potential bias caused by baseline characteristics differences in different trials through propensity score matching with aggregated data reported in other studies, and complete the comparison of the efficacy between target interventions. This study introduced the concept and principles of MAIC. In addition, we demonstrated how to use the anchored MAIC method based on R language for survival data, which has been widely used in anti-cancer drug evaluation. This study aimed to provide an alternative method to inform evidence-based decisions.
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.