Compute_S01: S0 and S1

In CompetingRisk: The Semi-Parametric Cumulative Incidence Function

Description

This function computes S0 and S1 in counting process.

Usage

Compute_S01(est, x, y, kk, event, group, save, 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 observations * p_k covariates.
y	The survival response associated with the k^th type of failure.
kk	The k^th type of failure.
event	This is an internal binary indicator to specify if any type of failure occurs at a given time.
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.
save	An option to save the computed S0 and S1. It is highly recommended for large-scale dataset to improve the computational efficiency. Default is save=FALSE.
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 computes

S^{(0,1)}(\hat{β}, t; z_{0}) = \frac{1}{n}∑_{i^{\prime}=1}^{n}I(\tilde{T}_{i^{\prime}} ≥ t) \exp(\hat{β}^{T}Z_{j})Z_{j}^{\otimes 0,1}

References

Andersen, Per Kragh, et al. Statistical models based on counting processes. Springer Science & Business Media, 2012.

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