Bernstein AFT Model

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

Usage

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

Arguments

formula

a Surv object with time to event observations, right censoring status and explanatory terms.

degree

Bernstein polynomial degree. If omitted, defaults to ceiling(sqrt(n)) for n = nrow(data) (see spbp).

data

a data.frame object.

approach

Bayesian or maximum likelihood estimation methods, default is approach = "mle".

dist

optional baseline specification; use bernstein(m) for the Bernstein polynomial degree.

baseline

optional alias for dist.

...

further arguments passed to or from other methods

Value

An object of class spbp, including component degree.

See also

spbp, bpph and bppo for other BP based models.

Examples

library("spsurv")
data("veteran", package = "survival")
#> Warning: data set ‘veteran’ not found

fit <- bpaft(Surv(time, status) ~ karno + celltype,
  data = veteran
)

summary(fit)
#> Call:
#> bpaft(formula = Surv(time, status) ~ karno + celltype, data = veteran, 
#>     approach = "mle", model = "aft")
#> 
#> Bernstein AFT model: 
#> Regression coefficients:
#>                   Estimate    2.5%   97.5% Std. Error z value Pr(>|z|)    
#> karno               0.0347  0.0251  0.0443     0.0049     7.1    1e-12 ***
#> celltypesmallcell  -0.7493 -1.3149 -0.1836     0.2886    -2.6    0.009 ** 
#> celltypeadeno      -0.9003 -1.4566 -0.3440     0.2838    -3.2    0.002 ** 
#> celltypelarge      -0.1343 -0.6891  0.4206     0.2831    -0.5    0.635    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Exponentiated coefficients:
#>                   Estimate 2.5% 97.5%
#> karno                 1.04 1.03   1.0
#> celltypesmallcell     0.47 0.27   0.8
#> celltypeadeno         0.41 0.23   0.7
#> celltypelarge         0.87 0.50   1.5
#> 
#> --- 
#> loglik = -710   AIC = 1451