Bernstein Polynomial Based Accelerated Failure Time Model

Fits the BPAFT model to time-to-event data.

bpaft(formula, degree, data, approach = c("mle", "bayes"), ...)

Arguments

formula	a Surv object with time to event observations, right censoring status and explanatory terms.
degree	Bernstein polynomial degree.
data	a data.frame object.
approach	Bayesian or maximum likelihood estimation methods, default is approach = "mle".
...	further arguments passed to or from other methods

Value

An object of class `spbp`.

See also

spbp, bpph and bppo for other BP based models.

Examples

library("spsurv")
data("veteran")

fit <- bpaft(Surv(time, status) ~ karno + celltype,
data = veteran)
#> Priors are ignored due to mle approach.

summary(fit)
#> Bernstein Polynomial based Accelerated Failure Time model
#> Call:
#> spbp.default(formula = Surv(time, status) ~ karno + celltype, 
#>     degree = degree, data = veteran, approach = "mle", model = "aft")
#> 
#>   n= 137, number of events= 128 
#> 
#>                       coef exp(coef) se(coef)     z Pr(>|z|)    
#> karno              0.03418   1.03477  0.00376  9.10  < 2e-16 ***
#> celltypesmallcell -0.69255   0.50030  0.24480 -2.83  0.00467 ** 
#> celltypeadeno     -0.86926   0.41926  0.24873 -3.49  0.00047 ***
#> celltypelarge     -0.12498   0.88252  0.26339 -0.47  0.63514    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Likelihood ratio test= 68.4  on 4 df,   p=5e-14
#> Wald test            = 140  on 4 df,   p=<2e-16