print.ate

Implementation of the following methods for event history analysis.
Risk regression models for survival endpoints also in the presence of competing
risks are fitted using binomial regression based on a time sequence of binary
event status variables. A formula interface for the Fine-Gray regression model
and an interface for the combination of cause-specific Cox regression models.
A toolbox for assessing and comparing performance of risk predictions (risk
markers and risk prediction models). Prediction performance is measured by the
Brier score and the area under the ROC curve for binary possibly time-dependent
outcome. Inverse probability of censoring weighting and pseudo values are used
to deal with right censored data. Lists of risk markers and lists of risk models
are assessed simultaneously. Cross-validation repeatedly splits the data, trains
the risk prediction models on one part of each split and then summarizes and
compares the performance across splits.

Thomas Alexander Gerds

riskRegression

Risk Regression Models and Prediction Scores for Survival
Analysis with Competing Risks

Johan Sebastian Ohlendorff

Paul Blanche

Rikke Mortensen

Marvin Wright

Nikolaj Tollenaar

John Muschelli

Ulla Brasch Mogensen

Brice Ozenne

print.ate function

<dl><dt>x</dt>
<dd>object obtained with function <code>ate</code></dd>
<dt>estimator</dt>
<dd>[character] The type of estimator relative to which the risks should be output.</dd>
<dt>...</dt>
<dd>for internal use</dd></dl>

Arguments

Print Average Treatment Effects — print.ate

<dl>

<dt>x</dt>
<dd>object obtained with function <code>ate</code></dd>


<dt>estimator</dt>
<dd>[character] The type of estimator relative to which the risks should be output.</dd>


<dt>...</dt>
<dd>for internal use</dd>

</dl>

Print Average Treatment Effects

print.ate: Print Average Treatment Effects

Description

Usage

Arguments

See Also