prep_cv2: Prepare for Cross validation index parameter selection

In HQM: Superefficient Estimation of Future Conditional Hazards Based on Marker Information

Prepare for Cross validation index parameter selection

Description

Implements the calculation of the hqm estimator on cross validation data sets. This is a preparation for the cross validation index selection technique for future conditional hazard rate estimation based on marker information data.

Usage

prep_cv2(in.par, data, data.id, marker_name1, marker_name2, event_time_name = 'years',
        time_name = 'year',event_name = 'status2', n, I, b)

Arguments

in.par	Vector of candidate indexing values.
data	A data frame of time dependent data points. Missing values are allowed.
data.id	An id data frame obtained from to_id.
marker_name1	The column name of the marker values in the data frame data.
marker_name2	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.
n	Number of individuals.
I	Number of observations leave out for a K cross validation.
b	Bandwidth.

Details

The function splits the data set via dataset_split and calculates for every splitted data set the hqm estimator

\hat{h}_x(t) = \frac{\sum_{i=1}^n \int_0^T\hat{\alpha}_i(\theta_0^T X_i(t+s))Z_i(t+s)Z_i(s)K_{b}(x-\theta_0^T X_i(s))\mathrm {d}s}{\sum_{i=1}^n\int_0^TZ_i(t+s)Z_i(s)K_{b}(x-\theta_0^T X_i(s))\mathrm {d}s},

for all x on the marker grid and t on the time grid, where X is the marker, Z is the exposure and \alpha(z) is the marker-only hazard, see get_alpha for more details.

Value

A list of matrices for every cross validation data set with \hat{h}_x(t) for all x on the marker grid and t on the time grid.

See Also

b_selection_index_optim

HQM documentation built on Jan. 8, 2026, 9:08 a.m.