PEP-methods

print,PEP-method

predict,PEP-method

PEP-methods
print.PEP: Print a PEP object to the console
predict.PEP: Predict for a novel patient from current PEP-ensemble

The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge <https://www.synapse.org/ProstateCancerChallenge> presented in Guinney J, Wang T, Laajala TD, et al. (2017) <doi:10.1016/S1470-2045(16)30560-5> is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects.

Teemu Daniel Laajala

ePCR

Ensemble Penalized Cox Regression for Survival Prediction

PEP-methods function

<dl><dt>x</dt>
<dd>Generic x</dd>
<dt>...</dt>
<dd>Additional custom parameters passed on</dd>
<dt>object</dt>
<dd>PEP-ensemble model object</dd>
<dt>type</dt>
<dd>Type of prediction; either "response" or "ensemble"</dd>
<dt>newx</dt>
<dd>New data matrix</dd>
<dt>x.expand</dt>
<dd>A function that may expand (i.e. extract features) from the input data matrix. By default this will be the default x.expand saved in the S4-slot of the first ensemble member. If the user wishes to omit this functionality, setting this parameter to 'x.expand = as.matrix' does not expand the input data matrix. Notice that if the user has manually called the 'conforminput' function for the newx-data, it is no longer necessary to expand the data matrix here.</dd></dl>

Arguments

PEP-methods — PEP-methods

<dl>

<dt>x</dt>
<dd>Generic x</dd>


<dt>...</dt>
<dd>Additional custom parameters passed on</dd>


<dt>object</dt>
<dd>PEP-ensemble model object</dd>


<dt>type</dt>
<dd>Type of prediction; either "response" or "ensemble"</dd>


<dt>newx</dt>
<dd>New data matrix</dd>


<dt>x.expand</dt>
<dd>A function that may expand (i.e. extract features) from the input data matrix. By default this will be the default x.expand saved in the S4-slot of the first ensemble member. If the user wishes to omit this functionality, setting this parameter to 'x.expand = as.matrix' does not expand the input data matrix. Notice that if the user has manually called the 'conforminput' function for the newx-data, it is no longer necessary to expand the data matrix here.</dd>

</dl>

PEP-methods: PEP-methods

Description

Usage

Arguments