Most of the COPD models to date follow a Markov structure, in which patients or populations can move between defined health states over successive time periods or cycles . In COPD, health states are typically based on disease severity defined solely by lung function, as described by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines. These current modeling methods may restrict the ability to reflect the disease progression/clinical pathway or clinical practice. This is related firstly to the memory-less nature of Markov modelling and secondly to the necessity to construct discrete health states
for each event or combination of events if it impacts the expected pay-off in a meaningful way. Markov models do not have the ability to retain memory of a patient’s previous health states without the introduction of additional health states or tunnel states . Most models currently lack the ability to describe a change in treatment , that is, a treatment switch or
re-initiation of therapy for a patient who has discontinued therapy without the addition of more health states to describe what should be a similar clinical pathway but with slightly different risks of clinical events occurring. Moreover, designing a model with more complex treatment pathways may encounter a lack of appropriate data on transition probabilities between health states and costs. Defining health states based on GOLD criteria is also often problematic as several clinical studies base their endpoint measurements on pre-bronchodilator measurements, rather than post-bronchodilator measurements as prescribed by GOLD once past the screening stage; this limits the applicability of these data in a model.
Most of the COPD models to date follow a Markov structure, in which patients or populations can move between defined health states over successive time periods or cycles . In COPD, health states are typically based on disease severity defined solely by lung function, as described by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines. These current modeling methods may restrict the ability to reflect the disease progression/clinical pathway or clinical practice. This is related firstly to the memory-less nature of Markov modelling and secondly to the necessity to construct discrete health states
for each event or combination of events if it impacts the expected pay-off in a meaningful way. Markov models do not have the ability to retain memory of a patient’s previous health states without the introduction of additional health states or tunnel states . Most models currently lack the ability to describe a change in treatment , that is, a treatment switch or
re-initiation of therapy for a patient who has discontinued therapy without the addition of more health states to describe what should be a similar clinical pathway but with slightly different risks of clinical events occurring. Moreover, designing a model with more complex treatment pathways may encounter a lack of appropriate data on transition probabilities between health states and costs. Defining health states based on GOLD criteria is also often problematic as several clinical studies base their endpoint measurements on pre-bronchodilator measurements, rather than post-bronchodilator measurements as prescribed by GOLD once past the screening stage; this limits the applicability of these data in a model.
การแปล กรุณารอสักครู่..
![](//thimg.ilovetranslation.com/pic/loading_3.gif?v=b9814dd30c1d7c59_8619)