multistate-package: Fitting Multistate Models

In multistate: Fitting Multistate Models

Description

Medical researchers are often interested in investigating the relationship between explicative variables and times-to-events such as disease progression or death. Multistate models allows multiple times-to-event to be studied simultaneously. Time-inhomogeneous Markov models consist of modelling the probabilities of transitions according to the chronological times (times since the baseline of the study). Semi-Markov (SM) models consist of modelling the probabilities of transitions according to the times spent in states. These SM models are becoming increasingly popular to deal with the complex evolution of chronic diseases.

In this package, we propose functions implementing usual 3-state and 4-state multistate models (SM models and time-inhomogeneous Markov models). We also propose to take into account the mortality of the general population (relative survival approach) and to estimate the marginal causal effects by adjusting for possible confounding factors using Inverse Probability Weighting (IPW). Finally, in the particular situation of one initial transient state and two competing and absorbing states, this package allows for estimating mixture models.

The methods proposed are limited to parametric models. The 3-state models model include one initial state (X=1), one transient state (X=2), and an absorbing state (X=3). In other words, this is an illness-death model. The possible transitions are: 1->2, 1->3 and 2->3. The 4-state models include one initial state (X=1), one transient state (X=2), and two competing and absorbing states (X=3 and X=4). The possible transitions are: 1->2, 1->3, 1->4, 2->3 and 2->4. The 2-state mixture model includes on initial transient state (X=1) and two absorbing states in competition (X=2 and X=3). The possible transitions are: 1->2 and 1->3.

Details

Package:	multistate
Type:	package
Version:	0.2
Date:	2017-08-02
License:	GPL (>=2)
LazyLoad:	yes

mm2	2-state mixture model for data with competing events
m3	3-state time-inhomogeneous Markov model
m3rs	3-state relative survival time-inhomogeneous Markov model with additive risks
m4	4-state time-inhomogeneous Markov model
m4rs	4-state relative survival time-inhomogeneous Markov model with additive risks
sm3	3-state semi-Markov model
sm3rs	3-state relative survival semi-Markov model with additive risks
sm3ic	3-state semi-Markov model with interval-censored data
sm4	4-state semi-Markov model
sm4rs	4-state relative survival semi-Markov model with additive risks
lrs.multistate	Likelihood ratio statistic to compare two embedded multistate models
pred.mm2	Predictions from a 2-state mixture model

Author(s)

Yohann Foucher <[e-mail]>

and Florence Gillaizeau <[e-mail]>

References

Gillaizeau F, Dantan E, Giral M, Foucher Y. A multistate additive relative survival semi-Markov model. Statistical methods in medical research Jun 2015. <DOI: 10.1177/ 0962280215586456>

Pohar M, Stare J. Relative survival analysis in R. Computer Methods and Programs in Biomedicine 2016; 81: 272-278. <DOI: 10.1016/ j.cmpb.2006.01.004>

Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res May 2011; 46(3): 399-424. <DOI: 10.1080/ 00273171.2011.568786>

Gillaizeau F, Senage T, Le Borgne F, Le Tourneau T, Roussel JC, Leffondre K, Porcher R, Giraudeau B, Dantan E, Foucher Y. Inverse Probability Weighting to control confounding in an illness-death model for interval-censored data. Manuscript in revision. 2016.

Trebern-Launay K, KesslerM, Bayat-Makoei S, Querard AH, Briancon S, Giral M, Foucher Y. Horizontal mixture model for competing risks: a method to obtain easily interpretable results by both physicians and patients-illustration for waitlisted renal transplant candidates in a perspective of patient-centered decision making. Manuscript submitted. 2017.

See Also

URL: www.labcom-risca.com

multistate documentation built on May 2, 2019, 5:16 a.m.