b_selection: Cross validation bandwidth selection

Description

Implements the bandwidth selection for the future conditional hazard rate $\hat h_x(t)$ based on K-fold cross validation.

Usage

b_selection(data, marker_name, event_time_name = 'years',
            time_name = 'year', event_name = 'status2', I, b_list)

Value

A list with the tested bandwidths and its cross validation scores.

Arguments

data: A data frame of time dependent data points. Missing values are allowed.
marker_name: The column name of the marker values in the data frame data.
event_time_name: The column name of the event times in the data frame data.
time_name: The column name of the times the marker values were observed in the data frame data.
event_name: The column name of the events in the data frame data.
I: Number of observations leave out for a K cross validation.
b_list: Vector of bandwidths that need to be tested.

Details

The function b_selection implements the cross validation bandwidth selection for the future conditional hazard rate $\hat h_x(t)$ given by $$ b_{CV} = arg min_b \sum_{i = 1}^N \int_0^T \int_s^T Z_i(t)Z_i(s)(\hat{h}_{X_i(s)}(t-s)- h_{X_i(s)}(t-s))^2 dt ds,$$ where $\hat h_x(t)$ is a smoothed kernel density estimator of $ h_x(t)$ and $Z_i$ the exposure process of individual $i$. Note that $\hat h_x(t)$ is dependent on $b$.

Examples

Run this code

# \donttest{
I = 26
b_list = seq(0.9, 1.3, 0.1)

b_scores_alb = b_selection(pbc2, 'albumin', 'years', 'year', 'status2', I, b_list)
b_scores_alb[[2]][which.min(b_scores_alb[[1]])]# }