Lambda_k: The cumulative hazards for the k^th type of failure.

In CompetingRisk: The Semi-Parametric Cumulative Incidence Function

Description

Estimate the cumulative hazards for the k^th type of failure.

Usage

Lambda_k(est, x, y, group, data, newdata, group.in.train)

Arguments

est	The model fitting details from the k^th cause-specific hazards model.
x	The design matrix associated with the k^th type of failure, of dimension n observation * p_k covariates.
y	The survival response associated with the k^th type of failure.
group	The name of the group covariates (if any). If specified, the cumulative hazards will be estimated for each group seperately. Default is group=NULL.
data	A data frame with n observations and p number of covariates.
newdata	A data frame or a matrix used for prediction. If not specified, the original data will be used instead.
group.in.train	This argument is valid only when the group argument is specified. If group is presented in both data and newdata, use group.in.train=T. If group is presented in only newdata but not data, use group.in.train=F.

Details

This function estimates the cumulative hazards for the k^th type of failure based on the observed data. It also introduces a flexible structure to predict the cumulative hazards for the k^th type of failure for a given new observation. When newdata has more than 2 observations, the mean values of the covariates will be used for prediction. When group is specified, the cumulative hazards for the k^th type of failure will be computed for each group seperately.

Value

It returns a data frame contains time of events, cumulative hazards for each type of failure, and group indicators (if any).

CompetingRisk documentation built on May 30, 2017, 2:54 a.m.