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
prca = get_prca_data()
## first create candidate subgroups
cand.groups <- subtee::subbuild(prca, dupl.rm = TRUE,
BM == 1, PF == 1, HX == 1,
STAGE == 4, AGE > 65, WT > 100)
fitdat <- cbind(prca, cand.groups)
subgr.names = names(cand.groups)
prog = as.formula(paste(" ~ ", paste0("`", names(cand.groups),"`", collapse = " + ")))
### Unadjusted estimates
res_unadj = unadj(resp = "SURVTIME", trt = "RX", subgr = subgr.names,
data = fitdat, covars = prog,
event = "CENS", fitfunc = "coxph")
### ModelAveraging estimates
res_modav = modav(resp = "SURVTIME", trt = "RX", subgr = subgr.names,
data = fitdat, covars = prog,
event = "CENS", fitfunc = "coxph")
### Bagged estimates
set.seed(321231) # set seed for reproducible results in the bootstrap samples
res_bagged = bagged(resp = "SURVTIME", trt = "RX", subgr = subgr.names,
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.

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.

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.

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

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

.

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

The `plot`

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

.

And it is also possible to compare