CSCNet is package with flexible tools for fitting and evaluating cause-specific cox models with elastic-net penalty. Each cause is modeled in a separate penalized cox model (using elastic-net penalty) with its exclusive \(\alpha\) and \(\lambda\) assuming other involved competing causes as censored.
In this package we will use Melanoma data from
‘riskRegression’ package (which will load up with ‘CSCNet’) so we start
by loading the package and the Melanoma data.
library(CSCNet)
library(riskRegression)
data(Melanoma)
as_tibble(Melanoma)
# A tibble: 205 x 11
time status event invasion ici epicel ulcer thick sex age logthick
<int> <dbl> <fct> <fct> <fct> <fct> <fct> <dbl> <fct> <int> <dbl>
1 10 2 death.ot~ level.1 2 prese~ pres~ 6.76 Male 76 1.91
2 30 2 death.ot~ level.0 0 not p~ not ~ 0.65 Male 56 -0.431
3 35 0 censored level.1 2 not p~ not ~ 1.34 Male 41 0.293
4 99 2 death.ot~ level.0 2 not p~ not ~ 2.9 Fema~ 71 1.06
5 185 1 death.ma~ level.2 2 prese~ pres~ 12.1 Male 52 2.49
6 204 1 death.ma~ level.2 2 not p~ pres~ 4.84 Male 28 1.58
7 210 1 death.ma~ level.2 2 prese~ pres~ 5.16 Male 77 1.64
8 232 1 death.ma~ level.2 2 not p~ pres~ 12.9 Male 49 2.56
9 232 2 death.ot~ level.1 3 not p~ pres~ 3.22 Fema~ 60 1.17
10 279 1 death.ma~ level.0 2 not p~ pres~ 7.41 Fema~ 68 2.00
# ... with 195 more rows
table(Melanoma$status)
0 1 2
134 57 14 There are 2 events in the Melanoma data coded as 1 & 2. To
introduce how setting up variables and hyper-parameters works in CSCNet,
we will fit the a model with the following hyper-parameters to the
Melanoma data: \[(\alpha_{1},\alpha_{2},\lambda_{1},\lambda_{2})=(0,0.5,0.01,0.02)\]
We set variables affecting the event: 1 as
age,sex,invasion,thick and variables affecting event: 2 as
age,sex,epicel,ici,thick.
In CSCNet, setting variables and hyper-parameters are done through named lists. Variables and hyper-parameters related to each involved cause are stored in list positions with the name of that position being that cause. Of course these names must be the same as values in the status variable in the data.
vl <- list('1'=c('age','sex','invasion','thick'),
'2'=~age+sex+epicel+ici+thick)
penfit <- penCSC(time = 'time',
status = 'status',
vars.list = vl,
data = Melanoma,
alpha.list = list('1'=0,'2'=.5),
lambda.list = list('1'=.01,'2'=.02))
penfit
$`Event: 1`
5 x 1 sparse Matrix of class "dgCMatrix"
1
age 0.008018578
sexMale 0.547580959
invasionlevel.1 0.756922406
invasionlevel.2 0.591044240
thick 0.118568171
$`Event: 2`
7 x 1 sparse Matrix of class "dgCMatrix"
1
age 0.04839997
sexMale 0.11419057
epicelpresent 0.16891622
ici1 -0.13501846
ici2 .
ici3 .
thick 0.03242932penfit is a comprehensive list with all information
related to the data and fitted models in detail that user can
access.
Note: As we saw, variable specification in
vars.list is possible in 2 ways which are introducing a
vector of variable names or a one hand sided formula for different
causes.
Now to obtain predictions, specially estimates of the absolute risks,
predict.penCSC method was developed so user can obtain
different forms of values in the easiest way possible. By this method on
objects of class penCSCS and for different involved causes,
user can obtain values for linear predictors (type='lp' or
type='link'), exponential of linear predictors
(type='risk' or type='response') and finally
semi-parametric estimates of absolute risks
(type='absRisk') at desired time horizons.
Note: Default value for event argument
in predict.penCSC is NULL. If user leaves it
as that, values for all involved causes will be returned.
Values of linear predictors for event: 1 related to 1st five individuals of the data:
predict(penfit,Melanoma[1:5,],type='lp',event=1)
# A tibble: 5 x 3
id event prediction
<int> <chr> <dbl>
1 1 1 2.72
2 2 1 1.07
3 3 1 1.79
4 4 1 0.913
5 5 1 2.99 Or the risk values of the same individuals for all involved causes:
predict(penfit,Melanoma[1:5,],type='response')
# A tibble: 10 x 3
id event prediction
<int> <chr> <dbl>
1 1 1 15.1
2 2 1 2.93
3 3 1 6.00
4 4 1 2.49
5 5 1 19.8
6 1 2 65.4
7 2 2 17.2
8 3 2 8.52
9 4 2 34.1
10 5 2 24.3 Now let’s say we want estimates of absolute risks related to the event: 1 as our event of interest at 3 and 5 year time horizons:
predict(penfit,Melanoma[1:5,],type='absRisk',event=1,time=365*c(3,5))
# A tibble: 10 x 4
id event horizon absoluteRisk
<int> <dbl> <dbl> <dbl>
1 1 1 1095 0.374
2 2 1 1095 0.0952
3 3 1 1095 0.187
4 4 1 1095 0.0798
5 5 1 1095 0.480
6 1 1 1825 0.525
7 2 1 1825 0.153
8 3 1 1825 0.292
9 4 1 1825 0.128
10 5 1 1825 0.654 Note: There’s also predictRisk.penCSC
to obtain absolute risk predictions. This method was developed for
compatibility with tools from ‘riskRegression’ package.
The above example was for illustration purposes. In real world
analysis, one must tune the hyper-parameters with respect to a proper
loss function through resampling procedures. tune_penCSC is
a comprehensive function that was built for this purpose on regularized
cause-specific cox models.
Like before, specification of variables and hyper-parameters are done
through named lists and sequences of candidate hyper-parameters related
to each involved cause are stored in list positions with the name of
that position being that cause. After that, tune_penCSC
will create all possible combinations from user’s specified sequences
and evaluates them using either IPCW brier score or IPCW AUC (as loss
functions) based on absolute risk predictions of the event of interest
(linking) through a chosen resampling process. Supported resampling
procedures are: cross validation (method='cv'), repeated
cross validation (method='repcv'), bootstrap
(method='boot'), Monte-Carlo or leave group out cross
validation (method='lgocv') and leave one out cross
validation (method='loocv').
tune_penCSC has the ability to automatically determine
the candidate sequences of \(\alpha\)
& \(\lambda\) values. Setting any
of alpha.grid & lambda.grid to
NULL will order the function to calculate them
automatically.
While the automatic sequence of \(\alpha\) values for all causes is
seq(0,1,.5), the process of determining the \(\lambda\) values automatically is by:
nlambdas.list.This will be done for each cause-specific model to create exclusive sequences of \(\lambda\)s for each of them.
If the data requires pre-processing steps, it must be done within the
resampling process to avoid data leakage. This can be achieved by using
preProc.fun argument of tune_penCSC function.
This arguments accepts a function that has a data as its only input and
returns a modified version of that data. Any pre-processing steps can be
specified within this function.
Note: tune_penCSC has the parallel
processing option. If a user has specified a function for pre-processing
steps with global objects or calls from other packages and wants to run
the code in parallel, the names of those extra packages and global
objects must be given through preProc.pkgs and
preProc.globals.
Now let’s see all that was mentioned in this section in an example. Let’s say we want to tune our model for 5 year absolute risk prediction of event: 1 based on time dependent (IPCW) AUC as the loss function (evaluation metric) through a 5-fold cross validation process:
#Writing a hypothetical pre-processing function
library(recipes)
Attaching package: 'recipes'
The following object is masked from 'package:stringr':
fixed
The following object is masked from 'package:stats':
step
std.fun <- function(data){
cont_vars <- data %>% select(where(~is.numeric(.))) %>% names
cont_vars <- cont_vars[-which(cont_vars %in% c('time','status'))]
#External functions from recipes package are being used
recipe(~.,data=data) %>%
step_center(all_of(cont_vars)) %>%
step_scale(all_of(cont_vars)) %>%
prep(training=data) %>% juice
}
#Tuning a regularized cause-specific cox
set.seed(455) #for reproducibility
tune_melanoma <- tune_penCSC(time = 'time',
status = 'status',
vars.list = vl,
data = Melanoma,
horizons = 365*5,
event = 1,
method = 'cv',
k = 5,
standardize = FALSE,
metrics = 'AUC',
alpha.grid = list('1'=0,'2'=c(.5,1)),
preProc.fun = std.fun,
parallel = TRUE,
preProc.pkgs = 'recipes')
Process was done in 35.32115 secs.
tune_melanoma$validation_result %>% arrange(desc(mean.AUC)) %>% head
alpha_1 alpha_2 lambda_1 lambda_2 horizon mean.AUC
1 0 0.5 0.1275 0.0700 1825 0.7424845
2 0 1.0 0.1275 0.0350 1825 0.7412018
3 0 0.5 0.1700 0.0350 1825 0.7391512
4 0 0.5 0.1700 0.0525 1825 0.7362160
5 0 0.5 0.1275 0.0175 1825 0.7350444
6 0 0.5 0.0850 0.0175 1825 0.7345937
tune_melanoma$final_params
$`1825`
alpha_1 alpha_2 lambda_1 lambda_2 horizon mean.AUC
1 0 0.5 0.1275 0.07 1825 0.7424845
tune_melanoma$final_fits
$`1825`
$`Event: 1`
5 x 1 sparse Matrix of class "dgCMatrix"
1
age 0.1215037
sexMale 0.4011456
invasionlevel.1 0.4872379
invasionlevel.2 0.3892797
thick 0.3048170
$`Event: 2`
7 x 1 sparse Matrix of class "dgCMatrix"
1
age 0.313738
sexMale .
epicelpresent .
ici1 .
ici2 .
ici3 .
thick .