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Produktbild: From Experimental Network to Meta-analysis
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From Experimental Network to Meta-analysis Methods and Applications with R for Agronomic and Environmental Sciences

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

17.05.2019

Abbildungen

X, 69 illus., 43 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Verlag

Springer Netherland

Seitenzahl

155

Maße (L/B/H)

24,1/16/1,5 cm

Gewicht

474 g

Auflage

1st ed. 2019

Sprache

Englisch

ISBN

978-94-024-1695-4

Beschreibung

Rezension

“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)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

17.05.2019

Abbildungen

X, 69 illus., 43 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Verlag

Springer Netherland

Seitenzahl

155

Maße (L/B/H)

24,1/16/1,5 cm

Gewicht

474 g

Auflage

1st ed. 2019

Sprache

Englisch

ISBN

978-94-024-1695-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: From Experimental Network to Meta-analysis
  • 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