There are various cycle correction method that exist, the most common being the halfcycle correction method. the half-cycle correction ), applied to the cumulative outcomes of an unadjusted discrete-time state-transition Markov model, will result in model output that is closer to that which would have been observed in the continuous time analogue. cumulative costs, or cumulative utilities), and it is common for some kind of correction method to be applied. Forcing transitions to occur only at discrete time steps leads to biased predictions of cumulative outcomes (e.g. A continuous-time Markov model is therefore a more natural representation for most disease and treatment processes. A discrete-time Markov model assumes that transitions between health states in a disease or treatment process occur at fixed intervals, but in reality, transitions between health states can usually occur at any point in time. Discrete-time state-transition Markov models are common in healthcare decision analytical modelling.
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