Fits a parametric accelerated failure time (AFT) regression model for
time-to-event data via survival::survreg().
Arguments
- formula
Model formula specifying the regression model, e.g.
Surv(time, event) ~ exposure.- data
Data set containing the variables of interest.
- dist
The AFT distribution type to use, defaulting to
"weibull".- ...
Other arguments passed to
survival::survreg().
Value
A survreg object with additional ertte_aft and ertte_model
classes used to supply additional methods.
Details
Like the survreg() function upon which it is based, ertte_aft()
supports four log-location-scale AFT models of the form
log(t) = mu + scale * w, where mu is the linear predictor
and the distribution of w is dependent on the choice of dist:
extreme-value distributions for "exponential" and
"weibull" models, a standard normal for "lognormal", and a
standard logistic for "loglogistic".
Because the return value is a survreg object, all the usual
methods for survival regression models work unchanged, without neeing an
ertte-specific equivalent. This includes summary(), coef(), vcov(),
confint(), predict(), AIC(), BIC(), logLik(), and anova().
Additional methods supplied via the ertte-specific classes include
ertte_predict(), ertte_fun(), and simulate().
Examples
# fit a Weibull AFT model
mod <- ertte_aft(Surv(time, event) ~ aucss, ertte_data)
mod
#> Call:
#> survival::survreg(formula = formula, data = data, dist = dist)
#>
#> Coefficients:
#> (Intercept) aucss
#> 4.8563375087 -0.0006407913
#>
#> Scale= 0.7164032
#>
#> Loglik(model)= -1207.3 Loglik(intercept only)= -1263.9
#> Chisq= 113.25 on 1 degrees of freedom, p= <2e-16
#> n= 300
# other AFT distributions are also supported
mod_ln <- ertte_aft(Surv(time, event) ~ aucss, ertte_data, dist = "lognormal")
mod_ln
#> Call:
#> survival::survreg(formula = formula, data = data, dist = dist)
#>
#> Coefficients:
#> (Intercept) aucss
#> 4.4908037548 -0.0006546191
#>
#> Scale= 0.978441
#>
#> Loglik(model)= -1222.3 Loglik(intercept only)= -1271.4
#> Chisq= 98.21 on 1 degrees of freedom, p= <2e-16
#> n= 300