simDat

sdat

fdat

simData

The dataset was generated based on the proposed model \(h(t; \boldsymbol{Z}_i, {X}_i(\cdot))=h_{0}(t-t_{i,j-1}) \exp \left(\eta_{ij}\right)\),
where \(h_0(\cdot)\) is the baseline hazard function generated from a Weibull distribution. \(\eta_{ij} = \bm{\alpha}^{\top}\boldsymbol{Z}_i +\int_{t_{i, j-1}}^{t}{X}_{i}(s)\beta(s)ds + v_{ij}\).
\(\bm{\alpha}\) is the fixed effect parameter associated with the time-invariant covariates \(\boldsymbol{Z}_i\),
and \(\beta(t)\) is a time-varying coefficient that captures the effect of functional predictor \(X_{i}(t)\) on the hazard rate of recurrent events.
The simulated dataset is organized into two data frames:
a survival data frame (<code>sdat</code>) and a functional data frame (<code>fdat</code>).
The variables in each data frame are listed below:

A collection of methods for modeling time-to-event data using both functional and scalar predictors. It implements functional data analysis techniques for estimation and inference, allowing researchers to assess the impact of functional covariates on survival outcomes, including time-to-single event and recurrent event outcomes.

Zifang Kong

FunSurv

Modeling Time-to-Event Data with Functional Predictors

Sy Han (Steven) Chiou

Yu-Lun Liu

simDat function

A list with two data frame:<dl>
<dt>sdat</dt>
<dd>Survival data; a data frame with xxx rows and xxx variables:</dd>
<dt>fdat</dt>
<dd>Functional data; a data frame with xxx rows and xxx variables:</dd></dl>

Format

Simulated datasets for demonstration — simDat

A list with two data frame:<dl>
<dt>sdat</dt>
<dd>Survival data; a data frame with xxx rows and xxx variables:</dd>










<dt>fdat</dt>
<dd>Functional data; a data frame with xxx rows and xxx variables:</dd>







</dl>

simDat: Simulated datasets for demonstration

Description

Usage

Arguments

Format