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Parametric survival regression

Source: R/surv_reg_survival.R
details_surv_reg_survival.Rd

survival::survreg() fits a parametric survival model.

Details

For this engine, there is a single mode: regression

Tuning Parameters

This model has 1 tuning parameters:

  • dist: Distribution (type: character, default: ‘weibull’)

Translation from parsnip to the original package

surv_reg(dist = character(1)) %>% 
  set_engine("survival") %>% 
  set_mode("regression") %>% 
  translate()

## Parametric Survival Regression Model Specification (regression)
## 
## Main Arguments:
##   dist = character(1)
## 
## Computational engine: survival 
## 
## Model fit template:
## survival::survreg(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg(), dist = character(1), model = TRUE)

Other details

Note that model = TRUE is needed to produce quantile predictions when there is a stratification variable and can be overridden in other cases.

The main interface for this model uses the formula method since the model specification typically involved the use of survival::Surv().

The model formula can include special terms, such as survival::strata(). The allows the model scale parameter to differ between groups contained in the function. The column used inside strata() is treated as qualitative no matter its type.

For example, in this model, the numeric column rx is used to estimate two different scale parameters for each value of the column:

library(survival)

surv_reg() %>% 
  fit(Surv(futime, fustat) ~ age + strata(rx), data = ovarian) %>% 
  extract_fit_engine()

## Call:
## survival::survreg(formula = Surv(futime, fustat) ~ age + strata(rx), 
##     data = data, model = TRUE)
## 
## Coefficients:
## (Intercept)         age 
##  12.8734120  -0.1033569 
## 
## Scale:
##      rx=1      rx=2 
## 0.7695509 0.4703602 
## 
## Loglik(model)= -89.4   Loglik(intercept only)= -97.1
##  Chisq= 15.36 on 1 degrees of freedom, p= 8.88e-05 
## n= 26

References

  • Kalbfleisch, J. D. and Prentice, R. L. 2002 The statistical analysis of failure time data, Wiley.