probtrans_ebmstate: Compute subject-specific transition probabilities using...

In ebmstate: Empirical Bayes Multi-State Cox Model

Compute subject-specific transition probabilities using convolution.

Description

Compute subject-specific transition probabilities using convolution.

Usage

probtrans_ebmstate(
  initial_state,
  cumhaz,
  model,
  max_time = NULL,
  nr_steps = 10000
)

Arguments

initial_state	The present function estimates transition probabilities from the state given in this argument.
cumhaz	An msfit object created by running mstate or mstate_generic.
model	Either 'clockforward' or 'clockreset'. See details.
max_time	The maximum time for which transition probabilities are estimated.
nr_steps	The number of steps in the convolution algorithm (larger increases precision but makes it slower)

Details

The clock-forward model is a model in which the transition hazard rates depend only on time since the initiating event. The clock-reset model has a single time scale: the sojourn time in the current state.

The algorithm behind probtrans_ebmstate is based on the convolution of density and survival functions and is suitable for processes with a tree-like transition structure only.

Value

An object of class 'probtrans'. See the 'value' section in the help page of mstate::probtrans.

Author(s)

Rui Costa & Moritz Gerstung

See Also

probtrans;

Examples

# Compute transition probabilities
# from an object with (pre-estimated) 
# cumulative hazard rates.

#load object with estimated
#cumulative hazard rates 
data("msfit_object_sample")

#compute transition probabilities
probtrans_object<-probtrans_ebmstate("health",
   msfit_object_sample,"clockforward")

ebmstate documentation built on Oct. 19, 2024, 5:08 p.m.