Flexible parametric spline learner. Calls flexsurv::flexsurvspline() from flexsurv.

## Details

The distr prediction is estimated using the fitted custom distributions from flexsurv::flexsurvspline() and the estimated coefficients however the prediction takes place in this package and not in flexsurv for a much faster and more efficient implementation.

As flexible spline models estimate the baseline hazard as the intercept, the linear predictor, lp, can be calculated as in the classical setting. i.e. For fitted coefficients, $$\beta = (\beta_0,...,\beta_P)$$, and covariates $$X^T = (X_0,...,X_P)^T$$, where $$X_0$$ is a column of $$1$$s: $$lp = \beta X$$.

## Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners\$get("surv.flexible")
lrn("surv.flexible")

## Parameters

 Id Type Default Levels Range bhazard untyped - - k integer 0 $$[0, \infty)$$ knots untyped - - bknots untyped - - scale character hazard hazard, odds, normal - timescale character log log, identity - inits untyped - - rtrunc untyped - - fixedpars untyped - - cl numeric 0.95 $$[0, 1]$$ maxiter integer 30 $$(-\infty, \infty)$$ rel.tolerance numeric 1e-09 $$(-\infty, \infty)$$ toler.chol numeric 1e-10 $$(-\infty, \infty)$$ debug integer 0 $$[0, 1]$$ outer.max integer 10 $$(-\infty, \infty)$$

## Custom mlr3 defaults

• k:

• Actual default: 0

• Adjusted default: 1

• Reason for change: The default value of 0 is equivalent to, and a much less efficient implementation of, LearnerSurvParametric.

Royston, Patrick, Parmar, KB M (2002). “Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects.” Statistics in medicine, 21(15), 2175--2197.