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State-transition models (STMs) are commonly used in health care to inform funding and reimbursement decisions of new technologies, to optimize the use of limited resources, or to guide public health policies. These models typically simulate progression of events (eg disease stage, death) that evolve over time under some uncertainty. The common applications of STMs stipulate that time advances in fixed discrete steps known as cycles (eg month or year), and state transitions occur only at the beginning or end of a cycle.

A critical step in building STMs is the choice of cycle length. This is determined by a number of factors, including availability of data and frequency of clinical follow-up. For example, an annual cycle length may be appropriate for a model evaluating the costeffectiveness of mammography screening; whereas, a weekly cycle length may be desired in modeling the cost-effectiveness of HIV treatment …