learner_surv_cox

Constructs a learner class object for fitting Cox proportional
hazards models.

Various methods for targeted and semiparametric inference including
augmented inverse probability weighted (AIPW) estimators for missing data and
causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>),
one-step imputation (Nordland et al (2026)) <doi:10.48550/arXiv.2606.07174>),
variable importance and conditional average treatment effects (CATE)
(van der Laan (2006) <doi:10.2202/1557-4679.1008>),
estimators for risk differences and relative risks (Richardson et al. (2017)
<doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized
linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

Klaus Holst

targeted

Targeted Inference

Klaus K. Holst

Benedikt Sommer

Andreas Nordland

Christian B. Pipper

learner_surv_cox function

<dl><dt>formula</dt>
<dd>(formula) Formula specifying response and design matrix.</dd>
<dt>info</dt>
<dd>(character) Optional information to describe the instantiated
learner object.</dd>
<dt>learner.args</dt>
<dd>(list) Additional arguments to
learner$new().</dd>
<dt>...</dt>
<dd>Additional arguments passed to lower-level functions (e.g., optimization controls).</dd></dl>

Arguments

Author

Construct a learner — learner_surv_cox

<dl>

<dt>formula</dt>
<dd>(formula) Formula specifying response and design matrix.</dd>


<dt>info</dt>
<dd>(character) Optional information to describe the instantiated
learner object.</dd>


<dt>learner.args</dt>
<dd>(list) Additional arguments to
learner$new().</dd>


<dt>...</dt>
<dd>Additional arguments passed to lower-level functions (e.g., optimization controls).</dd>

</dl>

learner_surv_cox: Construct a learner

Description

Usage

Value

Arguments

Author

Examples