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. 2013 Jul;54 Suppl 1(Suppl 1):i49-55.
doi: 10.1093/jrr/rrt040.

Data-driven Markov models and their application in the evaluation of adverse events in radiotherapy

Daniel Abler  1 , Vassiliki Kanellopoulos, Jim Davies, Manjit Dosanjh, Raj Jena, Norman Kirkby, Ken Peach
Affiliations

Data-driven Markov models and their application in the evaluation of adverse events in radiotherapy

Daniel Abler et al. J Radiat Res. 2013 Jul.

Abstract

Decision-making processes in medicine rely increasingly on modelling and simulation techniques; they are especially useful when combining evidence from multiple sources. Markov models are frequently used to synthesize the available evidence for such simulation studies, by describing disease and treatment progress, as well as associated factors such as the treatment's effects on a patient's life and the costs to society. When the same decision problem is investigated by multiple stakeholders, differing modelling assumptions are often applied, making synthesis and interpretation of the results difficult. This paper proposes a standardized approach towards the creation of Markov models. It introduces the notion of 'general Markov models', providing a common definition of the Markov models that underlie many similar decision problems, and develops a language for their specification. We demonstrate the application of this language by developing a general Markov model for adverse event analysis in radiotherapy and argue that the proposed method can automate the creation of Markov models from existing data. The approach has the potential to support the radiotherapy community in conducting systematic analyses involving predictive modelling of existing and upcoming radiotherapy data. We expect it to facilitate the application of modelling techniques in medical decision problems beyond the field of radiotherapy, and to improve the comparability of their results.

Keywords: Markov model; adverse events; decision analytic modelling; health informatics; radiotherapy.

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Figures

Fig. 1.
Fig. 1.
Automatic creation of Markov models from distinct data sources, based on common Markov Model Template (MMT).
Fig. 2.
Fig. 2.
State transition diagram for a general Markov model describing adverse events after radiotherapy. Generalization from [12].
Fig. 3.
Fig. 3.
Main concepts of ‘Markov Model Template’ (MMT) language for the description of generic Markov models.
Fig. 4.
Fig. 4.
MMT representation of generic Markov model for acute severe adverse events.
Fig. 5.
Fig. 5.
MMT representation of generic Markov model for chronic severe adverse events.
See this image and copyright information in PMC

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