Ayilya M wrote: ↑21 Apr 2020, 12:04
When doing Markov Modeling, if the sample size is not sufficient , is it right to do simulation method to obtain data for the study? If it is possible , what are simulation methods that can be used?
@Ayilya M
Basically in Pharmaceconomics, Bigger the data, you will get better results & you must always try for a bigger samples from the available resources.
Markov models are particularly useful when a decision problem involves a risk that is ongoing over time. A proper subset of population is used for this analysis ( That Might be small or big) . The model assumes that the patient is always in one of a finite number of states of health referred to as Markov states. All events of interest are modeled as transitions from one state to another.
The newer representation of Markov models, the Markov-cycle tree, uses a tree representation of clinical events and may be evaluated either as a cohort simulation or as a Monte Carlo simulation.
Monte Carlo simulation model can be developed using Basic MS Excel or R studio (R Language) Open Source. If you are planing for high number simulated runs ( More than 1 Million ) for example, you must prefer R Language Packages.
You can start your learning using EXCEL models.