In this vignette we showcase the various plots can be made with the package.

We first start producing the treatment effect estimates for all subgroups, using the `unadj`

, `modav`

and `bagged`

functions.

```
library(ggplot2)
library(subtee)
################################################################################
# We use the dataset from Rosenkranz (2016) https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201500147
# to illustrate the methods proposed in this work.
# The data comes from a clinical trial of an prostate cancer
# treatment
# Data is loaded from Royston, Patrick, and Willi Sauerbrei.
# Multivariable model-building: a pragmatic approach to
# regression anaylsis based on fractional polynomials for
# modelling continuous variables. Vol. 777. John Wiley & Sons, 2008.
# https://www.imbi.uni-freiburg.de/Royston-Sauerbrei-book
= get_prca_data()
prca #> Downloading remote dataset.
## first create candidate subgroups
<- subtee::subbuild(prca, dupl.rm = TRUE,
cand.groups == 1, PF == 1, HX == 1,
BM == 4, AGE > 65, WT > 100)
STAGE <- cbind(prca, cand.groups)
fitdat = names(cand.groups)
subgr.names = as.formula(paste(" ~ ", paste0("`", names(cand.groups),"`", collapse = " + ")))
prog
### Unadjusted estimates
= unadj(resp = "SURVTIME", trt = "RX", subgr = subgr.names,
res_unadj data = fitdat, covars = prog,
event = "CENS", fitfunc = "coxph")
### ModelAveraging estimates
= modav(resp = "SURVTIME", trt = "RX", subgr = subgr.names,
res_modav data = fitdat, covars = prog,
event = "CENS", fitfunc = "coxph")
### Bagged estimates
set.seed(321231) # set seed for reproducible results in the bootstrap samples
= bagged(resp = "SURVTIME", trt = "RX", subgr = subgr.names,
res_bagged data = fitdat, covars = prog,
event = "CENS", fitfunc = "coxph",
select.by = "BIC", B = 200) #B = 2000)
```

The objects resulting from calling `unadj`

, `modav`

and `bagged`

are `subtee`

objects that contain the results in a format that can be used to produce plots. For example, the following produces a forest plot showing treatment effect estimates for the subgroups and their complements:

```
ggplot(aes(y = Subset, x = trtEff, xmin = LB, xmax = UB, colour = Subset),
data = res_unadj$trtEff) +
geom_point(size = 2) +
geom_errorbarh(size = 1, show.legend = FALSE, height = 0) +
facet_grid(Group ~ .)
```

`plot`

function provided in the packageThe default option for the generic plot function in the package for `subtee`

objects shows the treatment effect in subgroups along with their confidence intervals.

```
plot(res_unadj)
#> Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
#> "none")` instead.
```

Note that only the treatment effect estimates in subgroups are displayed. Setting the option `show.compl = TRUE`

displays the treatment effect estimates in both subgroups and complements.

`plot(res_unadj, show.compl = TRUE)`

When using the `plot`

function to `subtee`

objects with unadjusted or model averaging estimates, the same layout is used. However, when the a `subtee`

object generated with the `bagged`

funciton is provided. it will only show the selected subgroup.

`plot(res_bagged, show.compl = TRUE)`

When more than one object is provided, the plot shows the comparison between different estimation techniques.

`plot(res_unadj, res_modav, palette = "Dark2")`

In this case it is again possible to set `show.compl = TRUE`

.

`plot(res_unadj, res_modav, show.compl = TRUE)`

And if bagged estimates are provided, it will only show the selected subgroup.

`plot(res_unadj, res_modav, res_bagged, show.compl = TRUE)`

The `plot`

function has also the option to show the treatment effect difference between subgroup and complement setting `type = "trtEffDiff"`

.

```
plot(res_unadj, type = "trtEffDiff")
#> Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
#> "none")` instead.
```

And it is also possible to compare

`plot(res_unadj, res_modav, type = "trtEffDiff")`

`plot(res_unadj, res_modav, res_bagged, type = "trtEffDiff")`