| flexmsm-package {flexmsm} | R Documentation |
flexmsm: Flexible Multi-State Modelling
Description
Provides a function for fitting any type of multistate survival model, with flexibly defined transition intensities and any type of observation scheme. The package also provides a host of tools for straightforward interpretation and visualisation of the fitted model.
The main fitting function is fmsm, which fits the multistate survival model, providing parameter and
as key inference quantities (p-values, estimated degrees of freedom, ...), as well as the elements needed to obtain
predicted transition intensities and probabilities, along with their confidence intervals.
The main auxiliary functions are Q.pred and P.pred.
Details
Provides functions for fitting and interpreting the output of general flexible multistate survival models.
The process is defined by means of a list of model specifications for the transition intensities, each of which follow syntax
similar to that used for GAMs in mgcv.
The estimation approach is based on a carefully structured, stable penalised likelihood approach, with the smoothers
(representing several types of covariate effects) set up using penalised regression splines.
The numerical routine carries out function minimization using a trust region algorithm in combination with an adaptation of an automatic
multiple smoothing parameter estimation procedure for Generalised Additive Models (see mgcv for more details on this last point). The smooths
supported by this package are those available in mgcv.
Confidence intervals for smooth components and nonlinear functions of the model parameters are derived using a Bayesian approach.
P-values for testing individual smooth terms for equality to the zero function are also provided and based on the approach
implemented in mgcv. The usual plotting and summary functions are also available.
Plots of the estimated transition intensities and transition probabilities can be obtained along with their respective confidence intervals. This includes 3D plots when two-dimensional splines are included in the model specification of one or more transition intensities.
Author(s)
Alessia Eletti (University College London, Department of Statistical Science), Giampiero Marra (University College London, Department of Statistical Science) and Rosalba Radice (Cass Business School, City, University of London).
Maintainer: Alessia Eletti alessia.eletti.19@ucl.ac.uk
References
Eletti, A., Marra, G., Radice, R., (submitted), A General Estimation Framework for Multi-State Markov Processes with Flexible Specification of the Transition Intensities.