Bernstein PO Model

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

Usage

bppo(
  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 bpaft for other BP based models.

Examples

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

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

summary(fit)
#> Call:
#> bppo(formula = Surv(time, status) ~ karno + celltype, data = veteran, 
#>     approach = "mle", model = "po")
#> 
#> Bernstein PO model: 
#> Regression coefficients:
#>                   Estimate    2.5%   97.5% Std. Error z value Pr(>|z|)    
#> karno              -0.0602 -0.0770 -0.0434     0.0086    -7.0    2e-12 ***
#> celltypesmallcell   1.2790  0.4198  2.1382     0.4384     2.9    0.004 ** 
#> celltypeadeno       1.4388  0.5074  2.3702     0.4752     3.0    0.002 ** 
#> celltypelarge       0.1239 -0.7910  1.0387     0.4668     0.3    0.791    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Exponentiated coefficients:
#>                   Estimate 2.5% 97.5%
#> karno                 0.94 0.93   1.0
#> celltypesmallcell     3.59 1.52   8.5
#> celltypeadeno         4.22 1.66  10.7
#> celltypelarge         1.13 0.45   2.8
#> 
#> --- 
#> loglik = -709   AIC = 1450