Baseline hazard function

Description

Computes the Breslow estimator of the baseline hazard function for a proportional hazard regression model

Usage

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basehaz.gbm(t, delta, f.x, 
            t.eval = NULL, 
            smooth = FALSE, 
            cumulative = TRUE)

Arguments

t

the survival times

delta

the censoring indicator

f.x

the predicted values of the regression model on the log hazard scale

t.eval

values at which the baseline hazard will be evaluated

smooth

if TRUE basehaz.gbm 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)). gbm can estimate the f(x) component via partial likelihood. After estimating f(x), basehaz.gbm can compute the a nonparametric estimate of lambda(t).

Value

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

Author(s)

Greg Ridgeway gregridgeway@gmail.com

References

N. Breslow (1972). "Disussion 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, gbm