"I would recommend the Meta-analysis in medical research book, or the R meta-analysis tutorials that compare different R packages." (Ramzi El Feghali, ISCB News, iscb.info, Issue 69, July, 2020)
Chapter 1. Introduction and examples Objectives of the analysis of experimental networks and meta-analysis Data The type of data The data collection Data validation Analysis Main steps Presentation of the tested hypotheses Collection of data Data validation Data analysis Validation of the analysis Communication of results Objective of the book A simple example of a mixed model Definition Data Model definition Estimate Comparison with the model without random effect References
Part I. Analysis of experimental networks
Chapter 2. Basic Concepts Agronomic experimentation Experimental network Definition Example of experiment network Environmental concept Objectives of network of experiments Concept of population of environments Interaction concept References
Chapter 3. Analysis of network of experiments in blocks of complete randomness as a studied factor Objective of the chapter Example "wheat" Modelization Model with a random experiment effect Model with a fixed experimental effect Example How to choose between a model with a fixed experimental effect and a model with a random experiment effect? Model evaluation Normality Homoscedasticity Independence Suspicious data Average comparisons Hypothesis tests: equality tests Confidence intervals Hypothesis tests: equivalence tests Example Example "wheat": R script and commented analysis References
Chapter 4. Advanced Methods for Network Analysis
Analysis of average data Step 1: Analysis of individual experiments to estimate treatment averages Step 2: Analysis of the average data Example A variant: analysis of average data with a fixed model Estimation of the interaction variance treatment-experimentation R script Experiments with heterogeneous variances Introduction Example "wheat" For further Missing data Origin of missing data Adjusted averages The factors place and year Goal Example "wheat_pluri" Model for analyzing average data Variance estimation of the treatment-year-place interaction Variance of the difference between two treatments Analysis of the example "wheat_pluri" and script R References
Chapter 5. Planning an Experimental Network Goal Comparison of two treatments Case of a multilocal network Case of a multi-local and multi-year network Other contrasts Average comparison of several witnesses Comparison to the overall average References
Part II. The meta-analysis
Chapter 6. Basics for meta-analysis Definition, origin and main stages of the meta-analysis Estimated average effect size Goal Systematic search of studies, selection of references and data extraction Estimation of the average effect size with a model without random effect Estimation of the average effect size with a random effects model Meta-regression Goal Example Regression models with and without random effect Example (continued) Critical analysis of results References
Chapter 7. Specific statistical problems for the meta-analysis Setting the effect size Correction of the bias related to the use of ratios Difference between observation means Effect sizes for binary data Correlation coefficient Effect sizes based on variance Generalized linear models for discrete data analysis Binomial logit model with random effects to analyze the effect of a treatment Example Mixed nonlinear models Interest and definition Example Bayesian models Definition Example: meta-analysis with MCMCglmm References
Annex. R resources to implement the methods of analysis networks and meta-analysis KenSyn package: R code and datasets of the examples presented in the different chapters Installation Content and use Implement the mixed model under R Adjust a mixed model Manipulate the results of mixed models under R The metafor package, dedicated to performing meta-analyzes under R Bayesian approach with the mixed model MCMCglmm package
Coda package References
Data analysis plays an increasing role in research, scientific expertise and prospective studies. Multiple data sources are often available to estimate a key parameter or to test a hypothesis of scientific or societal interest. These data, obtained under different environmental conditions or based on different experimental protocols, are generally heterogeneous. Sometimes they are not even directly accessible and should be extracted from scientific articles or reports. However, a comprehensive analysis of the available data is essential to increase the accuracy of estimates, assess the validity of research conclusions and understand the origin of the variability of the experimental results. A quantitative synthesis of the data set available allows for a better understanding of the effects of explanatory factors and for evidence-based recommendations.
Designed as a methodological guide, this book shows the interests and limitations of different statistical methods to analyze data from experimental networks and to perform meta-analyses. It is intended for engineers, students and researchers involved in data analysis in agronomy and environmental science. Our objective is to present the main statistical methods to analyze data from experimental networks and scientific publications. Each chapter exposes one or more methods and illustrates them with examples processed with the R software. Data and R codes are provided and commented in order to facilitate their adaptation to other situations. The codes can be reused from the KenSyn R package associated with this book.