lambda_j_Exp: Compute lambda_j

In eventTrack: Event Prediction for Time-to-Event Endpoints

Compute lambda_j

Description

For given change points \tau_1, \ldots, \tau_k, compute the profile maximum likelihood estimates \lambda_1, \ldots, \lambda_{k + 1} for the values of the piecewise constant hazard function in a piecewise Exponential survival model. Standard errors for these estimates are provided as well, based on standard maximum (profile) maximum likelihood theory.

Usage

lambda_j_Exp(tau, time, event)

Arguments

tau	Single number or vector with values of change points.
time	Event times, censored or observed, in months.
event	Censoring indicator, 1 for event, 0 for censored.

Value

A list containing the following elements:

aj	The esimtates of the \lambda_j.
hess.diag	The diagonal of the corresponding Hessian matrix. Note that the off-diagonal elements of the Hessian are all equal to 0.

Note

This function is not intended to be invoked by the end user.

Author(s)

Kaspar Rufibach (maintainer)
[e-mail]

References

Fang, L., Zheng, S. (2011). A hybrid approach to predicting events in clinical trials with time-to-event outcomes. Contemp. Clin. Trials, 32, 755–759.

Goodman, M.S., Li, Y., Tiwari, R.C. (2011). Detecting multiple change points in piecewise constant hazard functions. J. Appl. Stat, 38(11), 2523–2532.

eventTrack documentation built on April 4, 2025, 2:34 a.m.