probtrans_fft: Compute subject-specific transition probabilities using a...

In ebmstate: Empirical Bayes Multi-State Cox Model

Compute subject-specific transition probabilities using a convolution algorithm based on the Fast Fourier transform.

Description

Compute subject-specific transition probabilities using a convolution algorithm based on the Fast Fourier transform.

Usage

probtrans_fft(initial_state, cumhaz, max_time, nr_steps = 10000)

Arguments

initial_state	The present function estimates state occupation probabilities from the state given in this argument.
cumhaz	An msfit object created by running mstate or mstate_generic.
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 time argument is crucial for precision. The density of time points and the upper time limit should be increased until the estimated curves become stable. A useful rule of thumb is to set the upper time limit to a time point in which the probability of each transient state is zero and the probability of each absorbing state is constant.

For the same approximation grid, probtrans_fft doesn’t always yield the same result as probtrans_ebmstate (semi-Markov version), even though they are meant to approximate exactly the same convolution. probtrans_ebmstate is sensitive to the grid interval size, but not such much to the maximum grid time. probtrans_fft is sensitive to both these parameters, as referred above.

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

See Also

probtrans; probtrans_ebmstate

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