04-forest_plots.Rmd

# Forest Plots

![](forest.jpg)

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Now that we created the **output of our meta-analysis** using the `metagen`, `metacont` or `metabin` functions in `meta` (see [Chapter 4.1](#fixed),[Chapter 4.2](#random) and [Chapter 4.3](#binary)), it is time to present the data in a more digestable way. **Forest Plots** are an easy way to do this, and it is conventional to report forest plots in meta-analysis publications.


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```{block,type='rmdinfo'}
Now that we created the **output of our meta-analysis** using the `metagen`, `metacont` or `metabin` functions in `meta` (see [Chapter 4.1](#fixed),[Chapter 4.2](#random) and [Chapter 4.3](#binary)), it is time to present the data in a more digestable way.

**Forest Plots** are an easy way to do this, and it is conventional to report forest plots in meta-analysis publications.
```

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## Generating a Forest Plot

To produce a forest plot, we use the meta-analysis output we just created (e.g., `m`, `m.raw`) und the `meta::forest()` function. I'll use my `m.hksj.raw` output from [Chapter 4.2.3](#random.raw) to create the forest plot

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```{r,echo=FALSE,warning=FALSE,message=FALSE}
load("metacont_data.RData")
metacont$Ne<-as.numeric(metacont$Ne)
metacont$Me<-as.numeric(metacont$Me)
metacont$Se<-as.numeric(metacont$Se)
metacont$Mc<-as.numeric(metacont$Mc)
metacont$Sc<-as.numeric(metacont$Sc)
library(meta)
library(metafor)
m.hksj.raw<-metacont(Ne,
        Me,
        Se,
        Nc,
        Mc,
        Sc,
        data=metacont,
        studlab=paste(Author),
        comb.fixed = FALSE,
        comb.random = TRUE,
        method.tau = "SJ",
        hakn = TRUE,
        prediction=TRUE,
        sm="SMD")
metacont$intervention.type<-c("PCI","PCI","Mindfulness","CBT","CBT","CBT")
```

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```{r,fig.width=11,fig.height=3,fig.align='center'}
forest(m.hksj.raw)
```

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Looks good so far. We see that the function plotted a forest plot with a **diamond** (i.e. the overall effect and its confidence interval) and a **prediction interval**. There are plenty of **other parameters** within the `meta::forest` function which we can use to modify the forest plot.
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```{r,fig.width=11,fig.height=4,fig.align='center'}
forest(m.hksj.raw)
```

Looks good so far. We see that the function plotted a forest plot with a **diamond** (i.e. the overall effect and its confidence interval) and a **prediction interval**.

There are plenty of **other parameters** within the `meta::forest` function which we can use to modify the forest plot.
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```{r,echo=FALSE}
library(knitr)
library(grid)
load("foresttable.RData")
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foresttable1<-foresttable[1:23,]
kable(foresttable1) %>%
  column_spec(3, width = "30em")
```

```{r,echo=FALSE}
library(knitr)
library(grid)
load("foresttable.RData")
foresttable2<-foresttable[24:48,]
kable(foresttable2) %>%
  column_spec(3, width = "30em")
```

```{r,echo=FALSE}
library(knitr)
library(grid)
load("foresttable.RData")
foresttable3<-foresttable[49:52,]
kable(foresttable3) %>%
  column_spec(3, width = "35em")
```

This is again just an overview. For all settings, type `?meta::forest` in your **console** to see more.Let's play around with the function a little now:

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kable(foresttable)
```

This is again just an overview. For all settings, type `?meta::forest` in your **console** to see more.

Let's play around with the function a little now:
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```{r,fig.width=9,fig.height=3.5,fig.align='center'}
forest(m.hksj.raw,
       sortvar=TE,
       xlim = c(-1.5,0.5),
       rightlabs = c("g","95% CI","weight"),
       leftlabs = c("Author", "N","Mean","SD","N","Mean","SD"),
       lab.e = "Intervention",
       pooled.totals = FALSE,
       smlab = "",
       text.random = "Overall effect",
       print.tau2 = FALSE,
       col.diamond = "blue",
       col.diamond.lines = "black",
       col.predict = "black",
       print.I2.ci = TRUE,
       digits.sd = 2
)

```

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Looks good so far! For special **layout types**, proceed to [Chapter 5.2](#layouttypes) now.


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Looks good so far! For special **layout types**, proceed to [Chapter 5.2](#layouttypes) now.

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## Layout types {#layouttypes}

The `meta::forest` function also has two **Layouts** preinstalled which we can use. Those layouts can be accessed with the `layout=` parameter.

* **"RevMan5"**. This layout is used for Cochrane reviews and generated by *Review Manager 5*.
* **"JAMA"**. This layout gives you a forest plot according to the guidelines of the *Journal of the American Medical Association* as output (see details [here](https://jamanetwork.com/journals/jama/pages/instructions-for-authors)).

The **RevMan** layout looks like this:

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```{r,fig.width=10,fig.height=4,fig.align='center'}
forest(m.hksj.raw,
       layout = "RevMan5",
       digits.sd = 2)

```
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The **JAMA** layout looks like this:

```{r,fig.width=7,fig.height=3,fig.align='center'}
forest(m.hksj.raw,
       layout = "JAMA",
       text.predict = "95% PI",
       col.predict = "black",
       colgap.forest.left = unit(15,"mm"))
```

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## Saving the forest plots

Let's say i want to save the JAMA version of my Forest Plot now. To do this, i have to reuse the code with which i plotted my forest plot, and put it between `pdf(file='name_of_the_pdf_i_want_to_create.pdf')` and `dev.off`, both in separate lines. This saves the plot into a PDF in my Working Directory. This way, i can export the plot in different formats (you can find more details on the saving options [here](#saving)).

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## Saving the forest plots

Let's say i want to save the JAMA version of my Forest Plot now. To do this, i have to reuse the code with which i plotted my forest plot, and put it between `pdf(file='name_of_the_pdf_i_want_to_create.pdf')` and `dev.off`, both in separate lines. This saves the plot into a PDF in my Working Directory.

This way, i can export the plot in different formats (you can find more details on the saving options [here](#saving)).

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**PDF**

```{r, eval=FALSE}
pdf(file='forestplot.pdf') 
forest.jama<-forest(m.hksj.raw,
       layout = "JAMA",
       text.predict = "95% PI",
       col.predict = "black",
       colgap.forest.left = unit(15,"mm"))
dev.off() 
```

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**PNG**

```{r, eval=FALSE}
png(file='forestplot.png') 
forest.jama<-forest(m.hksj.raw,
       layout = "JAMA",
       text.predict = "95% PI",
       col.predict = "black",
       colgap.forest.left = unit(15,"mm"))
dev.off() 
```

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**Scalable Vector Graphic**

```{r, eval=FALSE}
svg(file='forestplot.svg') 
forest.jama<-forest(m.hksj.raw,
       layout = "JAMA",
       text.predict = "95% PI",
       col.predict = "black",
       colgap.forest.left = unit(15,"mm"))
dev.off() 
```


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