basehaz: Alias for the survfit function
In survival: Survival Analysis

basehaz

R Documentation

Alias for the survfit function

Description

Compute the predicted survival curve for a Cox model.

Usage

basehaz(fit, newdata, centered=TRUE)

Arguments

`fit`	a coxph fit
`newdata`	a data frame containing one row for each predicted survival curve, said row contains the covariate values for that curve
`centered`	ignored if the `newdata` argument is present. Otherwise, if TRUE return data from a predicted survival curve for the covariate values `fit$mean`, if FALSE return a prediction for all covariates equal to zero.

Details

This function is an alias for survfit.coxph, which does the actual work and has a richer set of options. Look at that help file for more discussion and explanation. This alias exists primarily because some users look for predicted survival estimates under this name.

The function returns a data frame containing the time, cumhaz and optionally the strata (if the fitted Cox model used a strata statement), which are copied from the survfit result.

If H(t; z) is the predicted cumulative hazard for an observation with covariate vector z, then H(t;x) = H(t;z) r(x,z) where r(x,z)= exp(beta[1](x[1]- z[1]) + beta[2](x[2]-z[2]) + ...) = exp(sum(coef(fit) * (x-z))) is the Cox model's hazard ratio for covariate vector x vs covariate vector z. That is, the cumulative hazard H for a single reference value z is sufficient to provide the hazard for any covariate values. The predicted survival curve is S(t; x)= exp(-H(t;x)). There is not a simple transformation for the variance of H, however.

Many textbooks refer to H(t; 0) as "the" baseline hazard for a Cox model; this is returned by the centered= FALSE option. However, due to potential overflow or underflow in the exp() function this can be a very bad idea in practice. The authors do not recommend this option, but for users who insist: caveat emptor. Offset terms can pose a particular challenge for the underlying code and are always recentered; to override this use the newdata argument and include the offset as one of the variables.