This small vignette gives more details on how the
`plot.adjustedsurv()`

function may be used to create
customized plots of the adjusted survival curves. Currently, this
function has over 60 different arguments that can be used to customize
the output, so itâ€™s very flexible. Not all arguments will be covered
here, but the vignette may help the user to obtain a better
understanding of the supported functionality.

To illustrate the different customization options we will use some
simulated data, which can be created easily using the
`sim_confounded_surv()`

function included in this
package.

```
library(ggplot2)
library(adjustedCurves)
set.seed(42)
data <- sim_confounded_surv(n=250, max_t=1.3)
data$group <- as.factor(data$group)
```

We will use an inverse probability of treatment weighted Kaplan-Meier estimator here to obtain the adjusted survival curves:

```
s_iptw <- adjustedsurv(data=data,
variable="group",
ev_time="time",
event="event",
method="iptw_km",
treatment_model=group ~ x1 + x3 + x5 + x6,
weight_method="glm",
conf_int=TRUE,
stabilize=TRUE)
```

Note that in a real data analysis, it would be necessary to carefully
check the underlying assumptions and to assess whether the chosen
weighting method results in reasonable confounder balance between the
groups. We also use `stabilize=TRUE`

here to ensure that the
sum of the weights equals the sample size, which makes the use of risk
tables easier.

Calling the `plot()`

function on this
`adjustedsurv`

object without any argument results in the
following plot:

This is already fairly decent, but it could be made much more informative as illustrated below.

To visually distinguish the survival curves, different colors are
used by default. By changing the `linetype`

and
`color`

arguments, it is however also possible to use black
and white plots instead:

If colors should be used, it may be usefull to change the colors
using the `custom_colors`

argument:

When using linetypes, users can also supply custom linetypes in a similar fashion:

Titles and axis labels may also be changed using the respective arguments:

When confidence intervals were estimated using
`conf_int=TRUE`

or `bootstrap=TRUE`

in the
original `adjustedsurv()`

function call, it is of course
possible to visualize them using the `plot()`

method as
well:

Note that the confidence level cannot be changed here. This can only
be done directly in the `adjustedsurv()`

function.

The median survival time is often used as a summary statistic and to
compare different survival curves. In this package, it may be calculated
using the `adjusted_surv_quantile()`

function. However, it
may also make sense to add some indicator lines to the plot to visually
help the reader to read off the median survival time from the plot as
well. This can be done using the `median_surv_lines`

argument:

The drawn lines may also be customized. Below we make the lines
thicker, change their color and change their linetype from
`"dashed"`

to `"dotdash"`

:

```
plot(s_iptw, median_surv_lines=TRUE, median_surv_linetype="dotdash",
median_surv_size=0.7, median_surv_color="grey")
```

It is also possible to use other survival time quantiles by changing
the `median_surv_quantile`

argument:

In standard Kaplan-Meier plots, the timing of censored observations
is often added to the plot as well. This can be done in this package as
well, using the `censoring_ind`

argument. We could, for
example, add small vertical lines to show the censored observations:

Alternatively, points of any shape could be added as well:

First, letâ€™s call the `plot()`

function with the default
risk table arguments:

Since we are using a weighted Kaplan-Meier estimator, the weighted
number at risk is shown by default. We could use the unweighted number
at risk by setting `risk_table_use_weights=FALSE`

, but this
wouldnâ€™t make much sense. Instead of using the weighted number at risk,
we can also use the weighted number of cumulative events:

or the weighted cumulative number of censored observations:

By setting `risk_table_stratify=TRUE`

, we are also able to
stratify the risk table by the different levels in
`variable`

:

Since weights are used here, there is no guarantee that the number of
people at risk are integer values. By default the numbers are rounded to
one digit to make this clear, but we can also set the
`risk_table_digits`

argument to 0 to round them to the
nearest integer, which may be a little less confusing for some
people:

The risk tables only show numbers at the break points of the x-axis
shown in the survival curve plot, to make everything align nicely. To
get more numbers at more points in time, we can simply augment the
`x_breaks`

or `x_n_breaks`

arguments, like
this:

The size and look of the numbers may be changed as well:

```
plot(s_iptw, risk_table=TRUE, risk_table_stratify=TRUE,
risk_table_digits=0, x_n_breaks=10, risk_table_size=3,
risk_table_family="serif", risk_table_fontface="italic")
```

Additionally, users may turn off the coloring of the numbers:

```
plot(s_iptw, risk_table=TRUE, risk_table_stratify=TRUE,
risk_table_digits=0, x_n_breaks=10,
risk_table_stratify_color=FALSE)
```

Or use different colors:

```
plot(s_iptw, risk_table=TRUE, risk_table_stratify=TRUE,
risk_table_digits=0, x_n_breaks=10,
risk_table_custom_colors=c("brown", "orange"))
```

The title and axis labels may of course also be changed or removed:

```
plot(s_iptw, risk_table=TRUE, risk_table_stratify=TRUE,
risk_table_digits=0, x_n_breaks=10,
risk_table_title="Weighted Number of people at risk",
risk_table_title_size=10, risk_table_title_position="middle",
risk_table_ylab=NULL)
```

Internally, separate plots are created for the survival curves and
for the risk table and put together afterwards using the
`plot_grid()`

function of the `cowplot`

package.
Because of this, users may also set different `ggplot2`

themes for the plots:

Usually, the `plot()`

function returns a standard
`ggplot2`

object that may be modified using standard
`ggplot2`

syntax. This, however, is no longer the case when
using risk tables, because the output now consists of two plots that
have been put together.

This means that this code works fine:

While the following code does not work:

To still allow users to use all standard `ggplot2`

options
when using risk tables we added two additional arguments:
`additional_layers`

and
`risk_table_additional_layers`

. Users may pass a list of
objects that can be added to a `ggplot2`

object to either of
these arguments. All objects in the list passed to
`additional_layers`

will be added to the survival curve plot
before putting it together with the risk table plot. Similarly, all
objects in the `risk_table_additional_layers`

list will be
applied to the risk table plot.

The preceding example could be fixed using the following code:

```
more_stuff <- list(theme(legend.position="top"))
plot(s_iptw, risk_table=TRUE, additional_layers=more_stuff)
```

In this particular case, we could have also simply set the
`legend.position`

to top, but this of course only works for
arguments directly supported by the `plot()`

method. Using
the arguments mentioned above, we can do quite a bit more, such as
adding more geoms:

```
more_stuff <- list(geom_hline(yintercept=0.7))
plot(s_iptw, risk_table=TRUE, additional_layers=more_stuff)
```

Which also works for the risk table subplot using the other argument:

In this particular case, we would probably use something similar to the following code to create a decent output:

```
plot(s_iptw, conf_int=TRUE, censoring_ind="lines", risk_table=TRUE,
risk_table_stratify=TRUE, risk_table_digits=0, x_n_breaks=10,
risk_table_title_size=11, median_surv_lines=TRUE,
gg_theme=theme_bw(), risk_table_theme=theme_classic(),
legend.position="top", custom_colors=c("blue", "red"),
xlab="Time in Years")
```

Of course this is just one of many possibilities.