coxN

coxN.cph

coxN.coxph

coxN.phreg

coxN.CauseSpecificCox

coxN.glm

Extract the number of observations from a Cox model

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

coxN function

<dl><dt>object</dt>
<dd>The fitted Cox regression model object either
obtained with <code>coxph</code> (survival package), <code>cph</code>
(rms package), or <code>phreg</code> (mets package).</dd></dl>

Arguments

Author

Extract the number of observations from a Cox model — coxN

<dl>

<dt>object</dt>
<dd>The fitted Cox regression model object either
obtained with <code>coxph</code> (survival package), <code>cph</code>
(rms package), or <code>phreg</code> (mets package).</dd>

</dl>

Extract the number of observations from a Cox model

coxN: Extract the number of observations from a Cox model

Description

Usage

Arguments

Author