optimize_bandwidth

Selects the optimal bandwidth by minimizing the Mean Squared Error (MSE) using cross-validation.

Nonparametric methods for landmark prediction of long-term survival outcomes, incorporating covariate and short-term event information. The package supports the construction of flexible varying-coefficient models that use discrete covariates, as well as multiple continuous covariates.
The goal is to improve prediction accuracy when censored short-term events are available as predictors, using robust nonparametric procedures that do not require correct model specification and avoid restrictive parametric assumptions found in alternative methods.
More information on these methods can be found in Parast et al. 2012 <doi:10.1080/01621459.2012.721281>,
Parast et al. 2011 <doi:10.1002/bimj.201000150>, and Parast and Cai 2013 <doi:10.1002/sim.5776>.
A tutorial for this package is available here: <https://www.laylaparast.com/landpred>.

Layla Parast

landpred

Landmark Prediction of a Survival Outcome

Dylan Huynh

optimize_bandwidth function

<dl><dt>landpred_obj</dt>
<dd>A landpred object.</dd>
<dt>t0</dt>
<dd>The landmark time.</dd>
<dt>tau</dt>
<dd>The prediction window.</dd>
<dt>lower</dt>
<dd>Lower bound for bandwidth search.</dd>
<dt>upper</dt>
<dd>Upper bound for bandwidth search.</dd>
<dt>transform</dt>
<dd>Transformation function for the short-term event (e.g., log). Default is identity.</dd>
<dt>reps</dt>
<dd>Number of cross-validation repetitions. Default is 50.</dd>
<dt>train_prop</dt>
<dd>Proportion of data used for training in each fold. Default is 0.66.</dd></dl>

Arguments

Optimize Bandwidth for Continuous Landpred Models — optimize_bandwidth

<dl>

<dt>landpred_obj</dt>
<dd>A landpred object.</dd>


<dt>t0</dt>
<dd>The landmark time.</dd>


<dt>tau</dt>
<dd>The prediction window.</dd>


<dt>lower</dt>
<dd>Lower bound for bandwidth search.</dd>


<dt>upper</dt>
<dd>Upper bound for bandwidth search.</dd>


<dt>transform</dt>
<dd>Transformation function for the short-term event (e.g., log). Default is identity.</dd>


<dt>reps</dt>
<dd>Number of cross-validation repetitions. Default is 50.</dd>


<dt>train_prop</dt>
<dd>Proportion of data used for training in each fold. Default is 0.66.</dd>

</dl>

optimize_bandwidth: Optimize Bandwidth for Continuous Landpred Models

Description

Usage

Value

Arguments