Finding the Evidence for Decision Analytic Cost Effectiveness Models

Edlin, Richard; McCabe, Christopher; Hulme, Claire; Hall, Peter; Wright, Judy

doi:10.1007/978-3-319-15744-3_2

Cited by 2 publications

(2 citation statements)

References 26 publications

(22 reference statements)

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“…Application of multistate models is not limited to biomedical studies like the evaluation of disease progression patterns (16)(17)(18) but cuts across various life history data, including health economics. In health economics studies inclined to the monitoring of disease progression, issues on the costeffectiveness of prevention strategies (19), treatment (20), and diagnosis intervention (21) to inform policy decision-making process (22) can be addressed using multistate models. There is an extensive review of multistate models in the literature.…”

Section: Figure1: Schematic Illustration Of Different Types Of Multismentioning

confidence: 99%

A Critical Review of Multistate Bayesian Models Modelling Approaches in Monitoring Disease Progression: Use of Kolmogorov-Chapman Forward Equations with in WinBUGS

2019

ARC Journal of Public Health and Community Medicine

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There are numerous fields of science in which multistate models are used, including biomedical researched and health economics. In biomedical studies, these stochastic continuous-time models are used to describe the time to event life history of an individual through a flexible framework for longitudinal data which can describe more than one possible time to event outcomes for a single individual. The standard estimation quantities in multistate models are transition probabilities and transition rates which can be mapped through the Kolmogorov-Chapman forward equations. Most multistate models assume the Markov property and time homogeneity; however, if these assumptions are violated an extension to non-Markovian and time-varying transition rates is possible. This manuscript extends reviews in various types of multistate models, assumptions, methods of estimations, data types and emerging software for fitting multistate models. We highlight strengths and limitations in multistate models for different software and emphasis is made on Multistate Bayesian models in Bayes X and Win BUGS software which are underutilized. A partially observed and aggregated dataset from the Zimbabwe national ART program is used to illustrate the use of Kolmogorov-Chapman forward equations in estimating transition rates from a three-state reversible multistate model based on viral load measurements in Win BUGS.

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Section: Figure1: Schematic Illustration Of Different Types Of Multismentioning

confidence: 99%

A Critical Review of Multistate Bayesian Models Modelling Approaches in Monitoring Disease Progression: Use of Kolmogorov-Chapman Forward Equations with in WinBUGS

2019

ARC Journal of Public Health and Community Medicine

View full text Add to dashboard Cite

show abstract

“…The application of multistate models is not limited to biomedical studies like the evaluation of disease progression patterns [13][14][15] but cuts across various life history data, including health economics. In health economics studies inclined to the monitoring of disease progression, issues on the cost-effectiveness of prevention strategies, 16 treatment, 17 and infectious disease diagnosis interventions like HIV 18 to inform various policy decision-making processes in HIV control programs, 19 can be addressed using multistate models.…”

Section: Introductionmentioning

confidence: 99%

A review of multistate modelling approaches in monitoring disease progression: Bayesian estimation using the Kolmogorov-Chapman forward equations

Matsena-Zingoni

Chirwa

Todd

et al. 2021

Stat Methods Med Res

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There are numerous fields of science in which multistate models are used, including biomedical research and health economics. In biomedical studies, these stochastic continuous-time models are used to describe the time-to-event life history of an individual through a flexible framework for longitudinal data. The multistate framework can describe more than one possible time-to-event outcome for a single individual. The standard estimation quantities in multistate models are transition probabilities and transition rates which can be mapped through the Kolmogorov-Chapman forward equations from the Bayesian estimation perspective. Most multistate models assume the Markov property and time homogeneity; however, if these assumptions are violated, an extension to non-Markovian and time-varying transition rates is possible. This manuscript extends reviews in various types of multistate models, assumptions, methods of estimation and data features compatible with fitting multistate models. We highlight the contrast between the frequentist (maximum likelihood estimation) and the Bayesian estimation approaches in the multistate modeling framework and point out where the latter is advantageous. A partially observed and aggregated dataset from the Zimbabwe national ART program was used to illustrate the use of Kolmogorov-Chapman forward equations. The transition rates from a three-stage reversible multistate model based on viral load measurements in WinBUGS were reported.

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Finding the Evidence for Decision Analytic Cost Effectiveness Models

Cited by 2 publications

References 26 publications

A Critical Review of Multistate Bayesian Models Modelling Approaches in Monitoring Disease Progression: Use of Kolmogorov-Chapman Forward Equations with in WinBUGS

A Critical Review of Multistate Bayesian Models Modelling Approaches in Monitoring Disease Progression: Use of Kolmogorov-Chapman Forward Equations with in WinBUGS

A review of multistate modelling approaches in monitoring disease progression: Bayesian estimation using the Kolmogorov-Chapman forward equations

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