R语言Meta分析实战教程：掌握数据分析关键

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"经典的R语言Meta分析教程"是一本由Guido Schwarzer、James R. Carpenter和Gerta Rücker合著的专业书籍，作为UseR!系列的一部分，它详细介绍了如何利用R语言这一强大的数据分析工具来进行Meta分析。Meta分析是一种统计学方法，用于整合多个独立研究的结果，以提供对某个效应或关联的更全面和准确的理解，特别适用于医学、社会科学和许多其他科学领域中的研究汇总。本书涵盖了Meta分析的基础理论，包括统计原理、效应大小的测量（如Hedges' g、OR和RR）、随机效应模型与固定效应模型的选择、异质性检验（如Q检验和I^2统计量）以及发表偏倚的评估。作者强调了R语言在Meta分析过程中的应用，提供了丰富的实例和代码，帮助读者逐步掌握如何在R环境中安装和使用metafor、rma.uni、rma.mv等专用于Meta分析的包。通过本书，读者不仅可以学习到如何运用R语言的灵活功能处理数据，清洗和整理元分析所需的原始数据，还能了解如何构建元分析模型，执行稳健的统计分析，并对结果进行解读和可视化。此外，书中还涉及了如何处理缺失值、多重比较调整、以及如何报告和展示Meta分析结果的标准化方式。对于R语言的初学者和Meta分析的实践者来说，这是一本实用的指南，帮助他们在实际工作中更高效地利用R进行深入的统计分析。无论是在学术研究还是在临床试验的数据整合中，这本书都为R用户提供了宝贵的资源。同时，随着电子版的发行，读者可以方便地在线获取和下载，随时随地学习和参考。"经典的R语言Meta分析教程"是一本不可或缺的参考资料，对提升数据分析能力具有显著价值。

4 1 An Introduction to Meta-Analysis in R

The data, and script ﬁles to repeat the examples, are downloadable from the website

http://meta-analysis-with-r.org/.

Established R users can probably skip the remainder of this chapter, in which we

introduce some key R concepts and illustrate them with a simple meta-analysis. The

aim in what follows is to get new users (who have used other statistics packages)

started with R so that they can follow the presentation in the remainder of the book.

For a more comprehensive introduction we recommend [1].

The remainder of this chapter is best read alongside a computer running R. Thus,

if you have not yet installed R, we would encourage you to do so: full installation

details are given in Appendix A.1.

1.1 Getting Started with R

To start R either press on the R desktop icon created during installation or select the

corresponding entry in the menu of installed applications. This should open the R

Console window, an example of which in Mac OS X is shown in Fig. 1.1.Ris

not a menu driven program, so the user must either type commands at the command

line, or execute a pre-written script. R is case sensitive, and most function names are

in lower case. Typing the function name shows the code; to execute a function we

must type parentheses. For example,

Fig. 1.1 Screenshot of R Console window under Mac OS X

1.2 Loading, Saving and Restoring Data 7

library” or “virtual directory”, which can contain many datasets, together with other

R objects such as functions. This workspace is stored as a ﬁle, with default name

.RData under Mac/Linux/Unix and _RData under Windows. Not only can we

have separate .RData ﬁles for different projects, we can make the contents of

as many as we like available to R. This can cause some confusion if they contain

different objects of the same name!

For this book, we will only need one .RData ﬁle. This we store in the current

Working directory. To ﬁnd the current working directory type getwd() at the R

command line.

Within R, objects are accessed by typing their names. Typical objects include

numbers, variables, vectors, matrices, data frames (approximately equivalent to

datasets in other packages) and functions.

To see the objects in the attached .RData workspace, use the ls function. After

doing the graphics demonstration in Sect. 1.1, you should see the following listing

of R objects if the workspace has been empty before; otherwise some additional R

objects will be listed.

> ls()

[1] "g" "lev" "n" "opar" "pie.sales" "pin"

[7] "scale" "usr" "x" "xadd" "xdelta" "xscale"

[13] "xx" "y" "yadd" "ydelta" "yscale" "yy"

In order to delete R object g, you can use R function rm:

> rm(g)

> ls()

[1] "lev" "n" "opar" "pie.sales" "pin" "scale"

[7] "usr" "x" "xadd" "xdelta" "xscale" "xx"

[13] "y" "yadd" "ydelta" "yscale" "yy"

To delete all R objects in the workspace (without any further warning!) you can

use the following command:

> rm(list=ls())

> ls()

character(0)

The result “character(0)” means that no R objects are found in the workspace.

Note, changes to the workspace are not permanent as long as you do not save

them in the ﬁle .RData. If R stops due to an internal error (which almost never

happens) or if you kill the R process, all changes to the workspace will be lost.

You can save the workspace at any time in an R session using R command

save.image() which saves the ﬁle .RData in the current working directory.

When you quit R (see Sect. 1.1.1) you also have the option to save the workspace in

the .RData ﬁle.

8 1 An Introduction to Meta-Analysis in R

To change the current working directory, type setwd("my_directory"),

replacing my_directory with the path to a pre-existing directory on your

computer.

Alternatively, the current working directory may be changed from the

toolbar under Mac OS and Windows.

We will now go through a sequence of commands to load, view and save data

from a meta-analysis. We will then go through a second sequence: restart R, reload

the data and view it. First, check the current working directory. At the command

prompt type getwd(). Next, outside R download the ﬁle dataset01.csv

from the website http://meta-analysis-with-r.org/ and save it in the current working

directory. These data come from a meta-analysis comparing Nedocromil sodium

with placebo for preventing exercise-induced bronchoconstriction [7]. To check it is

there, use the following command:

> list.ﬁles(pattern="dataset01")

[1] "dataset01.csv"

This will list all ﬁles in the current working directory containing the text

“dataset01”, in this case only the ﬁle “dataset01.csv”.

We can now load these data into R with the commands given in Fig. 1.2.The

str command shows that R object data1 is a data frame, i.e. a dataset. The data

consist of 17 studies; for each we have

•theauthor,

•theyear of publication,

• Ne,theN

umber of patients (sample size) in the experimental treatment group,

• Me,theM

ean response among the patients in the experimental group,

• Se,theS

tandard deviation of the response in the experimental group, and

• Nc, Mc and Sc the sample size, mean response and standard deviation in the

ontrol patients.

Finally, we simply type save.image() to save the data. Satisfy yourself this

has worked. Quit R not saving the data: q("no"). Now restart R, if you are not

using the default working directory change to your working directory, then type

load(".RData") and type data1. All being well, the data should still be there.

Note, instead of saving the whole workspace, a subset of R objects, e.g.

datasets and/or functions, can be saved in a ﬁle for access in future R sessions

using the save function. For example, in order to save R object data1 in

the ﬁle “data1.rda” in the current working directory we can use the command

save(data1, file="data1.rda"); the extension “.rda” is recommended

for ﬁles containing R objects. The ﬁle data1 can be loaded into R using the

command load("data1.rda").

The easiest way to create a new directory is to use the Explorer under Windows, the Finder under

Mac OS, or your preferred ﬁle manager under Linux.

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