seRobustCox

the number of time points at which to evaluate the standard errors of the predictions.

nTimes

One or several strings that match (either in lower or upper case or mixtures) one
or several of the strings <code>"hazard"</code>,<code>"cumhazard"</code>, <code>"survival"</code>.

type

the baseline hazard estimate returned by <code>BaseHazStrata_cpp</code>.

Lambda0

the value of the influence function returned by <code>iidCox</code>.

the number of observations in the dataset used to estimate the object.

object.n

nStrata

the linear predictor evaluated for the new observations

new.eXb

the variables involved in the linear predictor for the new observations

new.LPdata

the strata indicator for the new observations

new.strata

the survival evaluated for the new observations

new.survival

can be "iid" to return the value of the influence function for each observation
"se" to return the standard error for a given timepoint

export

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

object

Compute the standard error associated to the predictions from Cox regression model using the functional delta method.

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.

seRobustCox: Computation of standard errors for predictions

Description

Usage

Arguments

Value