Table of contents
Topic 1: Meta analysis topic selection and bibliometric analysis CiteSpace application
Topic 2: Meta analysis and R language data cleaning and related applications
Topic 3: Meta-analysis and exquisite graphics in R language
Topic 4: Meta regression analysis in R language
Topic 5: R language Meta diagnosis analysis and advanced
Topic 6: Uncertainty in Meta-analysis in R language and Bayesian application
Topic 7: Application of Deep Expansion Machine Learning in Meta-analysis
Meta-analysis is a method for collecting, merging and quantitative statistical analysis of research results from different sources according to a clear search strategy, selection and screening of literature standards, and strict evaluation methods for a certain scientific research problem. It first appeared in "evidence-based medicine". ", has been widely used in agriculture and forestry ecology, resource environment and other aspects . The R language has a complete and effective data processing, statistical analysis and storage mechanism, which can directly analyze and display the data. The command format is simple and the results are highly readable. It includes many software packages for Meta analysis, and is the best choice for Meta integration analysis and evaluation. effective platform. This tutorial focuses on the principles, formulas, operation steps and result analysis of Meta analysis, as well as advanced applications. It combines multiple examples to master the entire process of Meta analysis and uncertainty analysis, and explains Meta analysis in combination with machine learning and other methods. Extended application of big data .
Topic 1: Meta-analysis topic selection and bibliometric analysis CiteSpace application
1. Topic selection and literature search for meta - analysis
1) What is Meta-analysis
2) Topic selection strategy for Meta analysis
3) Literature search database
4) Accurate retrieval strategy, how to retrieve complete and accurate retrieval
5) Literature management and cleaning, how to formulate literature inclusion and exclusion criteria
6) Literature data acquisition skills
7) Bibliometric analysis of CiteSpace and analysis of research hotspots
Topic 2: Meta analysis and R language data cleaning and related applications
2. Common methods of Meta analysis and R language application
1) The advantages of R language for Meta analysis and its classic case application in " Nature " and " Science "
2) Basic operation of R language
3) R language data cleaning method
4) Explanation and practice of commonly used packages and related plug-ins for Meta analysis in R language
From self-programming calculation to calling Meta packages ( meta, metafor, dmetar, esc, metasens, metamisc, meta4diag, gemtc , robvis , netmeta , brms , etc.), the whole process analyzes how to perform meta calculation, meta diagnosis, Bayesian meta, mesh Meta, subgroup analysis, meta regression and graphing .
Topic 3: Meta-analysis and exquisite graphics in R language
3. R Language Meta Analysis
1) The process of Meta analysis in R language
2) Calculation of various meta effect values and cumulative effect values
RR, MD and SMD of continuous data
RR and OR for categorical data
3) Meta subgroup analysis
4) R language graphic visualization method
5) How to draw a beautiful forest map with ggplot2
Topic 4: Meta regression analysis in R language
4. R language Meta regression analysis
1) Meta regression statistical analysis theory and application
2) Similarities and differences between Meta regression and ordinary regression analysis
3) Fixed effect and random effect analysis
4) Drawing of bubble diagram ( bubble )
Topic 5: R language Meta diagnosis analysis and advanced
5. Advanced meta diagnosis in R language
1) Meta diagnostic analysis (t2, I2, H2, Q and other statistics)
2) Heterogeneity test
3) Sensitivity analysis
4) Bias analysis
5) Risk Analysis
Topic 6: Uncertainty in Meta-analysis in R language and Bayesian application
6. Uncertainty of Meta-analysis in R Language
1) Network Meta-analysis
2) Bayesian theory
3) R language Bayesian tools Stan , JAGS and brms
4) Bayesian Meta analysis and uncertainty analysis
Topic 7: Application of Deep Expansion Machine Learning in Meta-analysis
7 Application of Machine Learning in Meta-analysis
1) The basis of machine learning and the advantages of Meta machine learning
2) Use of Meta Weighted Random Forest (MetaForest)
3) Use Meta machine learning to integrate big data in the literature
4) Driver Analysis Using Machine Learning
Topic 8: Discussion and Q&A
1 exercise
2 Discussion and Q&A