The goal of {KMunicate} is to produce Kaplan–Meier plots in the style
recommended following the KMunicate study
(TP Morris *et al*. Proposals on Kaplan–Meier plots in medical
research and a survey of stakeholder views: KMunicate. *BMJ
Open*, 2019, 9:e030215).

You can install {KMunicate} from CRAN by typing the following in your R console:

`install.packages("KMunicate")`

Alternatively, you can install the dev version of {KMunicate} from GitHub with:

```
# install.packages("devtools")
::install_github("ellessenne/KMunicate-package") devtools
```

```
library(survival)
library(KMunicate)
```

The {KMunicate} package comes with a couple of bundled dataset,
`cancer`

and `brcancer`

. The main function is
named `KMunicate`

:

```
<- survfit(Surv(rectime, censrec) ~ hormon, data = brcancer)
KM <- seq(0, max(brcancer$rectime), by = 365)
time_scale KMunicate(fit = KM, time_scale = time_scale)
```

```
<- survfit(Surv(studytime, died) ~ drug, data = cancer2)
KM <- seq(0, max(cancer2$studytime), by = 7)
time_scale KMunicate(fit = KM, time_scale = time_scale)
```

You also might wonder, does this work with a single arm? Yes, yes it does:

```
<- survfit(Surv(studytime, died) ~ 1, data = cancer2)
KM <- seq(0, max(cancer2$studytime), by = 7)
time_scale KMunicate(fit = KM, time_scale = time_scale)
```

Finally, you can also plot 1 - survival by using the argument
`.reverse = TRUE`

:

```
<- survfit(Surv(rectime, censrec) ~ hormon, data = brcancer)
KM <- seq(0, max(brcancer$rectime), by = 365)
time_scale KMunicate(fit = KM, time_scale = time_scale, .reverse = TRUE)
```

By default, `KMunicate()`

will build a risk table conform
to the KMunicate style, e.g., with cumulative number of events and
censored (the column-wise sum is equal to the total number of
individuals at risk per arm):

```
<- survfit(Surv(rectime, censrec) ~ hormon, data = brcancer)
KM <- seq(0, max(brcancer$rectime), by = 365)
time_scale KMunicate(fit = KM, time_scale = time_scale)
```

Alternatively, it is possible to customise the risk table via the
`.risk_table`

argument. For instance, if one wants to have
interval-wise number of events and censored, just pass the
`survfit`

value to the `.risk_table`

argument:

`KMunicate(fit = KM, time_scale = time_scale, .risk_table = "survfit")`

This is the default output of the `summary.survfit()`

function.

Finally, it is also possible to fully omit the risk table by setting
`.risk_table = NULL`

:

`KMunicate(fit = KM, time_scale = time_scale, .risk_table = NULL)`

Assuming you have set up your computer to use custom fonts with
`ggplot2`

, customising your KMunicate-style plot is trivial.
All you have to do is pass the font name as the `.ff`

argument:

```
<- survfit(Surv(studytime, died) ~ 1, data = cancer2)
KM <- seq(0, max(cancer2$studytime), by = 7)
time_scale KMunicate(fit = KM, time_scale = time_scale, .ff = "Times New Roman")
```

Several options to further customise each plot are provided, see e.g. the introductory vignette for more details.