Imprecise Transition Matrices for Markov Cohort Models: Lower and Upper Expectations with a Practical Health Economic Application

Rowan Iskandar

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arXiv · 2606.25716

Imprecise Transition Matrices for Markov Cohort Models: Lower and Upper Expectations with a Practical Health Economic Application

Rowan Iskandar

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Abstract

In applied health research, Markov cohort models are built on a precisely specified transition probability matrix. However, in many applications, the available evidence -- transition counts, structural constraints, and treatment-effect data -- identifies a set of admissible matrices rather than one uniquely justified matrix. This paper formulates an imprecise-probability extension in which inference yields lower and upper expectations over an evidence-compatible set of precise Markov cohort models. The contribution differs from existing imprecise Markov-chain work by focusing on finite-horizon cohort trajectories, additive accumulated outcomes, and transition matrices constructed from empirical transition counts. Under non-empty compact separately specified outgoing-row sets, the lower and upper accumulated outcomes are computed exactly by Bellman-style lower and upper transition operators. We prove the envelope theorem, reduction to the classical model, coherence properties of the lower transition operator, and algebraic conditions under which a single selected matrix yields a non-robust decision. We then show how multinomial transition counts induce admissible matrix sets through the Imprecise Dirichlet Model. A real-world cost-effectiveness example of patent foramen ovale closure after cryptogenic stroke illustrates the practical consequence: the empirical transition matrix slightly favors closure, whereas the imprecise analysis yields an incremental net monetary benefit interval crossing zero. The method provides both a rigorous lower-expectation formulation and a practical diagnostic for decisions that depend on transition probabilities not fully resolved by the evidence.

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BibTeX RIS

Rowan Iskandar. 2026-06-24. Imprecise Transition Matrices for Markov Cohort Models: Lower and Upper Expectations with a Practical Health Economic Application. https://arxiv.org/abs/2606.25716

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