Bernstein Polynomial Based Proportional Hazards Model

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

bpph(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, bppo and bpaft for other BP based models.

Examples

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

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

summary(fit)
#> Bernstein Polynomial based Proportional Hazards model
#> Call:
#> spbp.default(formula = Surv(time, status) ~ karno + factor(celltype), 
#>     degree = degree, data = veteran, approach = "mle", model = "ph")
#> 
#>   n= 137, number of events= 128 
#> 
#>                               coef exp(coef) se(coef)     z Pr(>|z|)    
#> karno                     -0.03102   0.96946  0.00516 -6.01  1.9e-09 ***
#> factor(celltype)smallcell  0.73615   2.08788  0.25280  2.91   0.0036 ** 
#> factor(celltype)adeno      1.13996   3.12665  0.29342  3.89   0.0001 ***
#> factor(celltype)large      0.32982   1.39072  0.27605  1.19   0.2322    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Likelihood ratio test= 59.9  on 4 df,   p=3e-12
#> Wald test            = 62.1  on 4 df,   p=1e-12