title: Changing Cycle Lengths in State-Transition Models: Challenges and Solutions.
abstract: The choice of a cycle length in state-transition models should be determined by the frequency of clinical events and interventions. Sometimes there is need to decrease the cycle length of an existing state-transition model to reduce error in outcomes resulting from discretization of the underlying continuous-time phenomena or to increase the cycle length to gain computational efficiency. Cycle length conversion is also frequently required if a new state-transition model is built using observational data that have a different measurement interval than the model's cycle length. We show that a commonly used method of converting transition probabilities to different cycle lengths is incorrect and can provide imprecise estimates of model outcomes. We present an accurate approach that is based on finding the root of a transition probability matrix using eigendecomposition. We present underlying mathematical challenges of converting cycle length in state-transition models and provide numerical approximation methods when the eigendecomposition method fails. Several examples and analytical proofs show that our approach is more general and leads to more accurate estimates of model outcomes than the commonly used approach. MATLAB codes and a user-friendly online toolkit are made available for the implementation of the proposed methods.
authors.affiliation: Institute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA (JC)
authors.forename: Jagpreet
authors.initials: J
authors.lastname: Chhatwal
authors.affiliation: School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA (SJ)
authors.forename: Suren
authors.initials: S
authors.lastname: Jayasuriya
authors.affiliation: Merck Research Laboratories, Merck & Co., North Wales, PA, USA (EHE)
authors.forename: Elamin H
authors.initials: EH
authors.lastname: Elbasha
mesh_terms: D000066491:Clinical Decision-Making; D003362:Cost-Benefit Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D064887:Observational Studies as Topic; D011336:Probability
keywords: Markov models; cost-effectiveness analysis; decision analysis; global health; mathematical models