Page 79 - Htain Manual

lowering blood pressure on long-term consequences such as coronary artery disease (CAD)

or mortality or quality of life, to model long-term consequences of the anti-hypertensive drug

on survival, life years and QALY.

The third limitation of a RCT was its inability of have a longer time horizon to capture

all costs consequences satisfactorily. A decision model can use a lifetime study horizon to

capture all costs and consequences which can accrue as a result of the intervention. Having

said that, however, it does not mean that this can be generated without a previous evidence.

So, a model, synthesizes evidence from various inputs to predict long-term costs and
consequences. Finally, model construction is not limited in terms of the number of scenarios

which it can potentially evaluate. So, it overcomes the last limitation of a RCT by enabling

comparision of several possible treatments or program interventions to deal with a given
health problem.

Two most commonly used decision models in EE are decision tree and Markov model.

Classically, a decision tree is a unidirectional flow of events which begins with the decision of

giving an intervention or not. This is followed by occurrence of different sequence of
outcomes which may continue to happen with a given probability or chance at each step in a

unidirectional way. The tree ultimately ends with a terminal event in which individual may

return to full health or may die. The major limitation of a decision tree is its unidirectional

flow. This may be suitable for acute disease conditions which follow a particular course since

their onset and the patient may either recover completely and live, or may live with some
long-term sequelae or may die. However, this may not be the case with chronic non-

communicable diseases. For example, a patient diagnosed with hypertension may not

necessarily remain hypertensive all his life. He may recover back to be normotensive state
with treatment, or may progress to a worse off health state. Modelling such chronic diseases

requires application of a Markov model which differs from a decision tree in allowing

transition from any one health state to any other health state, which is biologically plausible

as per the scientific understanding of disease course.

The subsequent sections illustrate the use of a decision tree and a Markov model for

better understanding.

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