survfit.deepAFT: Compute a Survival Curve from a deepAFT or a deepSurv Model

In dnn: Deep Neural Network Tools for Probability and Statistic Models

Compute a Survival Curve from a deepAFT or a deepSurv Model

Description

Computes the predicted survival function of a previously fitted deepAFT or deepSurv model.

Usage

## S3 method for class 'deepAFT' or 'deepSurv'
## S3 method for class 'dSurv'
survfit(formula, se.fit=TRUE, conf.int=.95, ...)

Arguments

formula	a deepAFT or deepSurv fit object.
se.fit	a logical value indicating whether standard errors shall be computed. Default is TRUE
conf.int	the level for a two-sided confidence interval on the survival curve. Default is 0.95
...	other unused arguments.

Details

survfit.dSurv is called to compuate baseline survival function S_T0(t) from the deepAFT model deepAFT, where T0 = T/exp(mu), or log(T) = log(T) - mu.

For the deepSurv model deepAFT, survfit.dSurv evaluates the Nelson-Aalen estimate of the baseline survival function.

The default method, survfit has its own help page. Use methods("survfit") to get all the methods for the survfit generic.

Value

survfit.deepAFT returns a list of predicted baseline survival function, cumulative hazard function and residuals.

surv	Predicted baseline survival function for T0=T/exp(mu).
cumhaz	Baseline cumulative hazard function, -log(surv).
hazard	Baseline hazard function.
varhaz	Variance of the baseline hazard.
residuals	Martingale residuals of the (deepAFT) model.
std.err	Standard error for the cumulative hazard function, if se.fit = TRUE.

See survfit for more detail about other output values such as upper, lower, conf.type. Confidence interval is based on log-transformation of survival function.

Author(s)

Bingshu E. Chen

See Also

The default method for survfit survfit, predict.dSurv

dnn documentation built on May 29, 2024, 1:48 a.m.