Page 85 - Htain Manual

state, the overall cost is computed. This is continued till all the patients have reached the

terminal stage or died – i.e. lifetime study horizon. Each HCC patient in respective health state

for a given cycle length.

This exercise is performed for both the scenarios, i.e. sorafenib and BSC. Finally,

incremental cost-effectiveness ratio is computed as the ratio of difference in costs and

difference in benefits.

Conclusion
Overall, an economic evaluation needs measurement on costs and effects for two or

more possible alternatives which are being compared. In order to do so, piggy-backing an

epidemiological RCT study which is being conducted to estimate efficacy is one option.

However, as we discussed, a RCT may have limitations in many contexts to generate robust

evidence for an EE. As a result, decision models usually become imperative. However, it needs
to be recognized that decision models are not free of inaccuracy. These decision models can

lead to erroneous findings due to several reasons – firstly, if the model structure if incorrect

and is not biologically plausible, then it leads to incorrect outputs. Secondly, a model is as
good as the values of parameters which are fed to generate output. Hence, any uncertainty

in the values of these parameters can lead to uncertainty in estimate of the incremental cost-

effectiveness ratio (ICER i.e. Ratio of the difference of the costs and the benefits of the two

interventions being studied). As a result, just as the 95% confidence interval is computed

around the estimate found in a RCT, it calls for a sensitivity analysis in a decision-model based
EE to compute 95% confidence intervals. Subsequently, it needs to be assessed whether the

null value for the ICER lies within the 95% bounds. Thirdly, the population group which is

considered in a decision model may not be representative of certain population groups. Sub-
group analysis is the way forward in such situations.

To conclude, one can say that each methodology has certain limitations. However, the

decision modelling can overcome several limitations of a RCT based EE. As a result, there is a

trend towards EE which are done using a decision model alone, or using a decision model
5
alongside a RCT . Such as decision model would need evidence for parameters, which could
be limited. However, the key would be to use as much robust data to parameterize the model

and then take a decision. After all, a policy maker or program manager or a clinician can have

two options to make a decision about the appropriate intervention – either wait for the best

73