State‐space size considerations for disease‐progression models

Regnier, Eva; Shechter, Steven M.

doi:10.1002/sim.5808

Cited by 11 publications

(45 citation statements)

References 23 publications

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“…It is important to note that for a process that is truly Markov on I states, a reduced-state model will not satisfy the Markov property (Regnier & Shechter, 2013). The sojourn time will be non-exponential for the merged states and bias can be expected through the misspecification.…”

Section: Summary and Discussionmentioning

confidence: 99%

Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Cassarly

Martin

Chimowitz

et al. 2017

Communications in Statistics - Simulation and Computation

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Ordinal outcomes collected at multiple follow-up visits are common in clinical trials. Sometimes, one visit is chosen for the primary analysis and the scale is dichotomized amounting to loss of information. Multistate Markov models describe how a process moves between states over time. Here, simulation studies are performed to investigate the type I error and power characteristics of multistate Markov models for panel data with limited non-adjacent state transitions. The results suggest that the multistate Markov models preserve the type I error and adequate power is achieved with modest sample sizes for panel data with limited non-adjacent state transitions.

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Section: Summary and Discussionmentioning

confidence: 99%

Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Cassarly

Martin

Chimowitz

et al. 2017

Communications in Statistics - Simulation and Computation

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show abstract

“…The aggregation of states in a Markov process is a powerful technique used in a variety of fields including computational biology, such as protein network interaction analysis (Petrov et al , 2012), reaction modeling (Ullah et al , 2012), single molecule photobleaching (Messina et al , 2006), or disease-progression models (Regnier and Shechter, 2013). Its application to phylogenetic models has not been systematically studied, although it has been implemented in some software (Lartillot and Philippe, 2004, e.g.…”

Section: Discussionmentioning

confidence: 99%

State aggregation for fast likelihood computations in molecular evolution

2016

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MotivationCodon models are widely used to identify the signature of selection at the molecular level and to test for changes in selective pressure during the evolution of genes encoding proteins. The large size of the state space of the Markov processes used to model codon evolution makes it difficult to use these models with large biological datasets. We propose here to use state aggregation to reduce the state space of codon models and, thus, improve the computational performance of likelihood estimation on these models.ResultsWe show that this heuristic speeds up the computations of the M0 and branch-site models up to 6.8 times. We also show through simulations that state aggregation does not introduce a detectable bias. We analyzed a real dataset and show that aggregation provides highly correlated predictions compared to the full likelihood computations. Finally, state aggregation is a very general approach and can be applied to any continuous-time Markov process-based model with large state space, such as amino acid and coevolution models. We therefore discuss different ways to apply state aggregation to Markov models used in phylogenetics.Availability and ImplementationThe heuristic is implemented in the godon package (https://bitbucket.org/Davydov/godon) and in a version of FastCodeML (https://gitlab.isb-sib.ch/phylo/fastcodeml).Supplementary information Supplementary data are available at Bioinformatics online.

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“…The aggregation of states in a Markov pro-cess is a powerful technique used in a variety of fields including computational biology, such as protein network interaction analysis (Petrov et al, 2012), reaction modeling (Ullah et al, 2012), single molecule photobleaching (Messina et al, 2006), or disease-progression models (Regnier and Shechter, 2013). Its application to phylogenetic models has not been systematically studied, although it has been implemented in some software (Lartillot and Philippe, 2004, e.g.…”

Section: Discussionmentioning

confidence: 99%

State aggregation for fast likelihood computations in molecular evolution

Davydov

Robinson‐Rechavi²,

Salamin³

2015

Preprint

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Codon models are widely used to identify the signature of selection at the molecular level and to test for changes in selective pressure during the evolution of genes encoding proteins. The large dimensionality of the Markov processes used to model codon evolution makes it difficult to use these models with large biological datasets. We propose here to use state aggregation to reduce the dimensionality of codon models and, thus, improve the computational performance of likelihood estimation on these models. We show that this heuristic speeds up the computations of the M0 and branch-site models up to 6.8 times. We also show through simulations that state aggregation does not introduce a detectable bias. We analysed a real dataset and show that aggregation provides highly correlated predictions compared to the full likelihood computations. Finally, state aggregation is a very general approach and can be applied to any continuoustime Markov process-based model with large dimensionality, such as amino acid and coevolution models. We therefore discuss different ways to apply state aggregation to Markov models used in phylogenetics.

show abstract

State‐space size considerations for disease‐progression models

Cited by 11 publications

References 23 publications

Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Assessing Type I error and power of multistate Markov models for panel data—A simulation study

State aggregation for fast likelihood computations in molecular evolution

State aggregation for fast likelihood computations in molecular evolution

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