baseline_hazard: Baseline hazard function

In gbm-developers/gbm3: Generalized Boosted Regression Models

Baseline hazard function

Description

Computes the Breslow estimator of the baseline hazard function for a proportional hazard regression model - only for censored survival data.

Usage

baseline_hazard(surv_times, delta, coxph_preds, eval_times=NULL, smooth=FALSE,
cumulative=TRUE)

Arguments

surv_times	the survival times - an atomic vector of doubles
delta	the censoring indicator - a vector same length as surv_times
coxph_preds	the predicted values of the regression model on the log hazard scale
eval_times	values at which the baseline hazard will be evaluated
smooth	if TRUE baseline_hazard will smooth the estimated baseline hazard using Friedman's super smoother supsmu
cumulative	if TRUE the cumulative survival function will be computed

Details

The proportional hazard model assumes h(t|x)=lambda(t)*exp(f(x)). gbmt can estimate the f(x) component via partial likelihood. After estimating f(x), baseline_hazard can compute a nonparametric estimate of lambda(t).

Value

a vector of length equal to the length of surv_times (or of length eval_times if eval_times is not NULL) containing the baseline hazard evaluated at t (or at eval_times if eval_times is not NULL). If cumulative is set to TRUE then the returned vector evaluates the cumulative hazard function at those values.

Author(s)

James Hickey, Greg Ridgeway [e-mail]

References

N. Breslow (1972). "Discussion of 'Regression Models and Life-Tables' by D.R. Cox," Journal of the Royal Statistical Society, Series B, 34(2):216-217.

N. Breslow (1974). "Covariance analysis of censored survival data," Biometrics 30:89-99.

See Also

survfit, gbmt

gbm-developers/gbm3 documentation built on April 28, 2024, 10:04 p.m.