Fits the BPPO model to time-to-event data.
Usage
bppo(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
Examples
library("spsurv")
data("veteran", package = "survival")
#> Warning: data set ‘veteran’ not found
fit <- bppo(Surv(time, status) ~ karno + celltype,
data = veteran
)
#> Priors are ignored because the MLE approach is used.
summary(fit)
#> Bernstein Polynomial based Proportional Odds model
#> Call:
#> spbp.default(formula = Surv(time, status) ~ karno + celltype,
#> degree = degree, data = veteran, approach = "mle", model = "po")
#>
#> n= 137, number of events= 128
#>
#> coef exp(coef) se(coef) z Pr(>|z|)
#> karno -0.06126 0.94058 0.00877 -6.98 2.9e-12 ***
#> celltypesmallcell 1.28841 3.62703 0.43724 2.95 0.0032 **
#> celltypeadeno 1.43774 4.21117 0.47368 3.04 0.0024 **
#> celltypelarge 0.10756 1.11356 0.46658 0.23 0.8177
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> exp(coef) exp(-coef) lower .95 upper .95
#> karno 0.941 1.063 0.925 0.957
#> celltypesmallcell 3.627 0.276 1.539 8.545
#> celltypeadeno 4.211 0.237 1.664 10.656
#> celltypelarge 1.114 0.898 0.446 2.779
#>
#> Likelihood ratio test= 73.3 on 4 df, p=5e-15
#> Wald test = 65.4 on 4 df, p=2e-13