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 "excerpt": "A multi-state model is a statistical model in which individuals move among a finite number of states, used to analyze disease progression in survival analysis.",
 "snippet": "A multi-state model is a statistical model in which individuals move among a finite number of states, used to analyze disease progression in survival analysis.",
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 "markdown": "# Multi-state model\n\nA multi-state model is a statistical model for a continuous-time stochastic process in which individuals move among a finite number of states, used to analyze disease progression and event histories in survival analysis and medical statistics.<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC2692556/)</sup> A change of state is a transition, or event; states can be transient or absorbing, death being the typical absorbing state.<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC2692556/)</sup> The model estimates transition intensities between states and, from them, quantities such as the probability of occupying a state at a given time and the expected time spent in a state.<sup>[2](https://link.springer.com/article/10.1007/s40471-022-00291-y)</sup> Ordinary survival analysis is the special case of two states, alive and dead<sup>[3](https://cran.r-project.org/web/packages/survival/vignettes/compete.pdf)</sup>; multi-state models extend it by modeling intermediate events such as relapse, disease onset, or transplant rejection, with each transition given its own intensity and covariate effects.<sup>[2](https://link.springer.com/article/10.1007/s40471-022-00291-y)</sup>\n\n| Key fact | Detail |\n|---|---|\n| Core parameter | The transition intensity \\( q_{rs}(t) \\), a rate (not a probability) collected in the intensity matrix \\( Q \\) whose rows sum to zero<sup>[4](https://gianluca.statistica.it/books/online/r-hta/chapters/11.multistate_models/multistate-models)</sup> |\n| Main outputs | State occupation probabilities, sojourn times, expected number of visits to a state<sup>[2](https://link.springer.com/article/10.1007/s40471-022-00291-y)</sup> |\n| Nonparametric estimator | The Aalen–Johansen estimator, the multistate analog of Kaplan–Meier; both the Kaplan–Meier estimator and the cumulative incidence function are special cases of it<sup>[2](https://link.springer.com/article/10.1007/s40471-022-00291-y)</sup><sup> • </sup><sup>[3](https://cran.r-project.org/web/packages/survival/vignettes/compete.pdf)</sup> |\n| Canonical example | The illness-death (disability) model, widely used to study disease incidence and rate of death<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC2692556/)</sup> |\n| Main variants | Markov, semi-Markov (clock-reset), and hidden Markov (misclassification) models<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC2692556/)</sup><sup> • </sup><sup>[5](https://www.degruyterbrill.com/document/doi/10.1515/ijb-2020-0083/html?lang=de)</sup> |\n| Software | Complete R workflows in msm, mstate, flexsurv, and survival (version 3.0 or later); narrower packages include SemiMarkov and penMSM<sup>[6](https://journal.r-project.org/articles/RJ-2024-002/RJ-2024-002.pdf)</sup> |\n| Sample size | Studies designed and powered for Cox analysis are generally too small; the cited proposal reported that overall sample sizes between 400 and 600 would fulfill its acceptance criteria, a range specific to that proposal rather than a general regulatory rule<sup>[7](https://pmc.ncbi.nlm.nih.gov/articles/PMC11015071/)</sup> |\n\n## How it works\n\nThe model is defined by a state space and transition intensities. The intensity \\( q_{rs}(t) \\) is the limit of \\( P_r(X(t+\\Delta t)=s \\mid X(t)=r)/\\Delta t \\) as \\( \\Delta t \\to 0 \\); it is the rate at which transitions from state \\( r \\) to state \\( s \\) occur, not a transition probability.<sup>[4](https://gianluca.statistica.it/books/online/r-hta/chapters/11.multistate_models/multistate-models)</sup>\n\nThe [Markov property](https://www.edgepedia.org/markov-property) buys tractability: transition probabilities are obtained from the intensities by solving the forward Kolmogorov differential equation,<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC2692556/)</sup> and in the time-homogeneous case the transition matrix over an interval of length \\( u \\) is the matrix exponential \\( P(t) = \\exp(t \\cdot Q) \\).<sup>[4](https://gianluca.statistica.it/books/online/r-hta/chapters/11.multistate_models/multistate-models)</sup> In the time-homogeneous case, a sojourn in state \\( r \\) is exponentially distributed with rate \\( -q_{rr} \\) and mean \\( -1/q_{rr} \\), and the probability that the next move from \\( r \\) is to \\( s \\) equals \\( -q_{rs}/q_{rr} \\); with time-varying intensities the holding-time distribution and destination probabilities are instead governed by the time-varying rates.<sup>[8](https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf)</sup>\n\nObservation enters through the likelihood. For panel data, where each individual is seen only at times \\( t_j, t_{j+1}, \\dots \\), the likelihood contribution of observed states is the transition-matrix entry \\( L_{i,j} = p_{S(t_j)S(t_{j+1})}(t_{j+1} - t_j) \\), and the full likelihood is the product of these terms over all individuals and intervals.<sup>[8](https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf)</sup> Right-censoring requires the independent censoring condition: censored individuals should have neither lower nor higher risk of future events than individuals who are not censored.\n\n## How it is done\n\nAn analysis proceeds in a standard sequence. The first step is to set up a transition matrix specifying which transitions are possible; data are then prepared in long format, where each row corresponds to a transition for which a patient is at risk, and a Cox-type model is fitted with `coxph` using `strata(trans)` for separate baseline hazards.<sup>[9](https://hputter.r-universe.dev/mstate/doc/Tutorial.pdf)</sup> The mstate package covers all steps from model building and data preparation to estimation and graphical representation for such non- and semi-parametric models.<sup>[10](https://scholarlypublications.universiteitleiden.nl/access/item%3A2873393/view)</sup>\n\nSeveral estimation routes exist. Nonparametrically, the Aalen–Johansen estimator is obtained by product integration of Nelson–Aalen estimates of the cumulative transition intensity matrix; the `survfit` function in the survival package implements it with standard errors via the infinitesimal jackknife.<sup>[3](https://cran.r-project.org/web/packages/survival/vignettes/compete.pdf)</sup> Parametrically, fully parametric Weibull-type intensity models can be fitted with flexsurv, as in a pharmacometrics tutorial using a five-state simulated model with 1000 patients and treatment as a covariate.<sup>[7](https://pmc.ncbi.nlm.nih.gov/articles/PMC11015071/)</sup> For panel data, maximum likelihood requires computing \\( P(t) \\) by the matrix exponential of the scaled intensity matrix, which is numerically delicate; msm uses eigensystem decomposition or Padé approximants with scaling and squaring.<sup>[8](https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf)</sup> Bayesian estimation maps transition rates to transition probabilities through the Kolmogorov–Chapman forward equations, illustrated with a three-stage reversible model on Zimbabwe ART program data fitted in [WinBUGS](https://www.edgepedia.org/winbugs).<sup>[11](https://journals.sagepub.com/doi/10.1177/0962280221997507)</sup>\n\n## Origin\n\nThe modern framework was consolidated in the counting-process methodology of the book *Statistical Models Based on Counting Processes* by Per Kragh Andersen, Ørnulf Borgan, Richard D. Gill, and Niels Keiding (Springer, 1993).<sup>[12](https://doi.org/10.1007/978-1-4612-4348-9)</sup> Philip Hougaard's review \"Multi-state Models: A Review\" (Lifetime Data Analysis, 1999) organized the field, noting that Markov models are much simpler from a probability point of view but often do not fit satisfactorily.<sup>[13](https://doi.org/10.1023/a:1009672031531)</sup> Per Kragh Andersen and Niels Keiding gave a general overview of multi-state models for event history analysis in 2002.<sup>[14](https://doi.org/10.1191/0962280202sm276ra)</sup> In the same year, Andersen, Steen Z Abildstrom, and Susanne Rosthøj treated the competing risks model as a special case of a multi-state model.<sup>[15](https://doi.org/10.1191/0962280202sm281ra)</sup> Jason P. Fine and Robert J. Gray introduced the subdistribution hazards model for competing risks in 1999.<sup>[16](https://doi.org/10.1080/01621459.1999.10474144)</sup> The main R infrastructure followed: mstate,<sup>[17](https://doi.org/10.1016/j.cmpb.2010.01.001)</sup> msm,<sup>[18](https://doi.org/10.18637/jss.v038.i08)</sup> and etm.<sup>[19](https://doi.org/10.18637/jss.v038.i04)</sup> Earlier methodological roots include Bruce W. Turnbull's 1976 estimator for arbitrarily grouped, censored, and truncated data, used for interval-censored problems.<sup>[20](https://doi.org/10.1111/j.2517-6161.1976.tb01597.x)</sup> Landmarking for dynamic prediction in event history analysis was introduced by Hans C. van Houwelingen in 2006.<sup>[21](https://doi.org/10.1111/j.1467-9469.2006.00529.x)</sup>\n\n## Variants\n\n**Markov models** assume the intensity depends on the history only through the current state, which simplifies likelihood evaluation but often fits unsatisfactorily.<sup>[13](https://doi.org/10.1023/a:1009672031531)</sup> **Semi-Markov (clock-reset) models** let the intensity depend on time since entry to the current state. Semi-Markov processes relax the memoryless exponential sojourn restriction, resetting the clock to zero after each transition; Weibull, Gompertz, and gamma hazard functions are commonly used, and simple exponential hazards may not correctly describe longer-scale disease progression such as cancer progression.<sup>[5](https://www.degruyterbrill.com/document/doi/10.1515/ijb-2020-0083/html?lang=de)</sup><sup> • </sup><sup>[7](https://pmc.ncbi.nlm.nih.gov/articles/PMC11015071/)</sup> In the mstate framework, clock-reset models are obtained by replacing `Surv(Tstart,Tstop,status)` with `Surv(time,status)`.<sup>[9](https://hputter.r-universe.dev/mstate/doc/Tutorial.pdf)</sup>\n\n**Hidden Markov (misclassification) models** allow the true state to be observed with error; the msm package fits continuous-time Markov and hidden Markov models to panel data with covariates on both transition rates and misclassification probabilities.<sup>[18](https://doi.org/10.18637/jss.v038.i08)</sup> msm implements continuous-time Markov and hidden Markov models for panel data; non-Markov likelihoods for intermittently observed processes are also possible with suitable models and methods, though they are more complex to specify and compute.<sup>[8](https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf)</sup>\n\nThe **illness-death model** (Health, Illness, Death) is the canonical example and the most common model in epidemiology for chronic diseases.<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC2692556/)</sup><sup> • </sup><sup>[5](https://www.degruyterbrill.com/document/doi/10.1515/ijb-2020-0083/html?lang=de)</sup> When death times are observed exactly but the pre-death state is unknown, msm sums the likelihood over the possible states: \\( L_{i,j} = \\sum_{m \\neq D} p_{S(t_j),m}(t_{j+1} - t_j)\\, q_{m,D} \\).<sup>[8](https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf)</sup> [Competing risks](https://www.edgepedia.org/competing-risks) form a special case with one transient alive state and absorbing death-from-cause states, with cause-specific hazards as transition intensities.<sup>[15](https://doi.org/10.1191/0962280202sm281ra)</sup>\n\n## Applications\n\nMulti-state models are used across medical statistics and beyond. The mstate documentation uses data from the EBMT (European Group for Blood and Marrow Transplantation) with a six-state, 12-transition model, supporting transition-specific covariates and separate baseline hazards per transition.<sup>[10](https://scholarlypublications.universiteitleiden.nl/access/item%3A2873393/view)</sup> The msm manual's worked example fits a four-state heart-transplant model (CAV-free, mild/moderate CAV, severe CAV, death) by maximizing the likelihood over seven unknown transition intensities.<sup>[8](https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf)</sup> Andersen and Keiding's overview uses the PROVA Danish multicenter liver cirrhosis trial, in which 286 patients were randomized between 1985 and 1989. In drug development, multistate-model survival criteria select promising oncology drugs for further development in phase I studies.<sup>[7](https://pmc.ncbi.nlm.nih.gov/articles/PMC11015071/)</sup> Compared with a Cox model that produces a single \"average\" relative risk, multi-state regression models each transition: in an NAFLD example, NAFLD was associated with mortality relative risk 2.16 (95% CI 1.41–3.31) with no metabolic comorbidities but 1.08 (95% CI 0.89–1.30) with three comorbidities.<sup>[2](https://link.springer.com/article/10.1007/s40471-022-00291-y)</sup>\n\n## Limitations and alternatives\n\n**Non-Markov mis-specification** is the central failure mode. The Aalen–Johansen estimator is consistent for transition probabilities only if the process is Markov, and may be biased otherwise.<sup>[22](https://onlinelibrary.wiley.com/doi/10.1111/biom.12288)</sup> Published comparisons agree, however, that AJ estimates of state occupation probabilities remain valid for non-Markov processes provided censoring is completely independent, a consistency result since proved rigorously and extended to left-truncated data with wild bootstrap inference.<sup>[23](https://ideas.repec.org/a/eee/ecosta/v25y2023icp110-124.html)</sup> Somnath Datta and Glen A. Satten (2001) studied the validity of the AJ and Nelson–Aalen estimators for non-Markov models.<sup>[24](https://doi.org/10.1016/s0167-7152%2801%2900155-9)</sup>\n\nDiagnostics exist. The Markov assumption can be checked by fitting \\( \\alpha_{23}(t; Z) = \\alpha_{23,0}(t)\\exp\\{\\beta Z\\} \\) with \\( Z \\) the time spent in the previous state and testing \\( H_0: \\beta = 0 \\).<sup>[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC2692556/)</sup> The markovMSM package, by Gustavo Soutinho and Luís Meira-Machado (2023), implements three approaches: history-dependent covariates, discrepancy of non-Markov transition-probability estimators from the Aalen–Johansen estimator, and summaries of families of log-rank statistics.<sup>[25](https://doi.org/10.32614/rj-2023-032)</sup> Andrew C. Titman and Hein Putter (2020) developed general tests of the Markov property.<sup>[26](https://doi.org/10.1093/biostatistics/kxaa030)</sup> When the Markov assumption fails, alternatives include estimators for the progressive illness-death model that are consistent regardless of the Markov condition and the censoring-support assumption.<sup>[22](https://onlinelibrary.wiley.com/doi/10.1111/biom.12288)</sup>\n\n**Observation conditions** matter. The Aalen–Johansen estimator requires exactly observed transition times and will be biased with interval censoring; the AJ method in `survfit` does not account for interval censoring, whereas msm handles such panel data, estimating time until progression occurred rather than time until progression was observed.<sup>[3](https://cran.r-project.org/web/packages/survival/vignettes/compete.pdf)</sup> In competing risks, the transition intensity is the cause-specific hazard, which has no simple direct relationship with the Fine–Gray subdistribution hazard,<sup>[2](https://link.springer.com/article/10.1007/s40471-022-00291-y)</sup> and presenting cause-specific hazards on a transformed scale is misleading because that transformation lacks a probability interpretation when other causes operate.<sup>[15](https://doi.org/10.1191/0962280202sm281ra)</sup>\n\nRecent developments include the ebmstate package, which extends mstate to high-dimensional data via an empirical Bayes (ridge-type) Cox model, adds an analytical Fourier-transform-based estimator of state occupation probabilities for clock-reset models, and replaces asymptotic with bootstrap confidence intervals.<sup>[6](https://journal.r-project.org/articles/RJ-2024-002/RJ-2024-002.pdf)</sup> Absolute-risk quantities such as time spent in a state and probability of being in a state may be more appropriate for causal inference than hazard ratios.<sup>[2](https://link.springer.com/article/10.1007/s40471-022-00291-y)</sup>\n\n## References\n\n1. [Multi-state models for the analysis of time-to-event data (Meira-Machado et al., Statistical Methods in Medical Research, 2009)](https://pmc.ncbi.nlm.nih.gov/articles/PMC2692556/)\n2. [The Utility of Multistate Models: A Flexible Framework for Time-to-Event Data (Current Epidemiology Reports, 2022)](https://link.springer.com/article/10.1007/s40471-022-00291-y)\n3. [Multi-state models and competing risks (Therneau, survival package vignette)](https://cran.r-project.org/web/packages/survival/vignettes/compete.pdf)\n4. [Continuous time multistate models – R for Health Technology Assessment](https://gianluca.statistica.it/books/online/r-hta/chapters/11.multistate_models/multistate-models)\n5. [Estimation of semi-Markov multi-state models: a comparison of the sojourn time and transition intensity approaches (International Journal of Biostatistics)](https://www.degruyterbrill.com/document/doi/10.1515/ijb-2020-0083/html?lang=de)\n6. [ebmstate: An R Package For Disease Progression Analysis with Multi-state Models (The R Journal, 2024)](https://journal.r-project.org/articles/RJ-2024-002/RJ-2024-002.pdf)\n7. [Application of multistate modeling to clinical data analysis (pharmacometrics tutorial, 2024)](https://pmc.ncbi.nlm.nih.gov/articles/PMC11015071/)\n8. [Multi-state modelling with R: the msm package (manual)](https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf)\n9. [mstate vignette: companion to Tutorial in Biostatistics: Competing risks and multi-state models (Putter et al. 2007)](https://hputter.r-universe.dev/mstate/doc/Tutorial.pdf)\n10. [mstate: An R Package for the Analysis of Competing Risks and Multi-State Models (de Wreede, Fiocco, Putter)](https://scholarlypublications.universiteitleiden.nl/access/item%3A2873393/view)\n11. [A review of multistate modelling approaches in monitoring disease progression: Bayesian estimation using the Kolmogorov-Chapman forward equations](https://journals.sagepub.com/doi/10.1177/0962280221997507)\n12. [Per Kragh Andersen and colleagues (1993). Statistical Models Based on Counting Processes. Springer series in statistics.](https://doi.org/10.1007/978-1-4612-4348-9)\n13. [Philip Hougaard (1999). Multi-state Models: A Review. Lifetime Data Analysis.](https://doi.org/10.1023/a:1009672031531)\n14. [Per Kragh Andersen, Niels Keiding (2002). Multi-state models for event history analysis. Statistical Methods in Medical Research.](https://doi.org/10.1191/0962280202sm276ra)\n15. [Per Kragh Andersen, Steen Z Abildstrom, Susanne Rosthøj (2002). Competing risks as a multi-state model. Statistical Methods in Medical Research.](https://doi.org/10.1191/0962280202sm281ra)\n16. [Jason P. Fine, Robert J. Gray (1999). A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association.](https://doi.org/10.1080/01621459.1999.10474144)\n17. [Liesbeth C. de Wreede, Marta Fiocco, Hein Putter (2010). The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models. Computer Methods and Programs in Biomedicine.](https://doi.org/10.1016/j.cmpb.2010.01.001)\n18. [Christopher H. Jackson (2011). Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software.](https://doi.org/10.18637/jss.v038.i08)\n19. [Arthur Allignol, Martin Schumacher, Jan Beyersmann (2011). Empirical Transition Matrix of Multi-State Models: TheetmPackage. Journal of Statistical Software.](https://doi.org/10.18637/jss.v038.i04)\n20. [Bruce W. Turnbull (1976). The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data. Journal of the Royal Statistical Society Series B (Statistical Methodology).](https://doi.org/10.1111/j.2517-6161.1976.tb01597.x)\n21. [HANS C. VAN HOUWELINGEN (2006). Dynamic Prediction by Landmarking in Event History Analysis. Scandinavian Journal of Statistics.](https://doi.org/10.1111/j.1467-9469.2006.00529.x)\n22. [Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study (de Uña-Álvarez & Meira-Machado, Biometrics 2015)](https://onlinelibrary.wiley.com/doi/10.1111/biom.12288)\n23. [Statistical inference for state occupation and transition probabilities in non-Markov multi-state models subject to both random left-truncation and right-censoring (Econometrics and Statistics, 2023)](https://ideas.repec.org/a/eee/ecosta/v25y2023icp110-124.html)\n24. [Validity of the Aalen–Johansen estimators of stage occupation probabilities and Nelson–Aalen estimators of integrated transition hazards for non-Markov models (Statistics & Probability Letters, 2001)](https://doi.org/10.1016/s0167-7152%2801%2900155-9)\n25. [Gustavo Soutinho, Luís Meira-Machado (2023). markovMSM: An R Package for Checking the Markov Condition in Multi-State Survival Data. The R Journal.](https://doi.org/10.32614/rj-2023-032)\n26. [Andrew C Titman, Hein Putter (2020). General tests of the Markov property in multi-state models. Biostatistics.](https://doi.org/10.1093/biostatistics/kxaa030)\n\n---\n*Topic: Encyclopedia › Physical world and mathematics › Mathematics and statistics › Statistics and probability › Stochastic processes › Markov chains and processes*\n\n*Initially written Sep 29, 2026 · Reviewed: Sep 30, 2026 · Edited: Sep 30, 2026 · Last review: Sep 30, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n",
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 "speakable": "A multi-state model is a statistical model in which individuals move among a finite number of states, used to analyze disease progression in survival analysis."
}