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:

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

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

and `brcancer`

. The main function is named `KMunicate`

:

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

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

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

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

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

:

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

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

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`

:

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:

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

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