Abstract
Cost-effectiveness analysis (CEA) provides information on how much extra do we need to spend per unit gain in health outcomes with introduction of any new healthcare intervention or treatment as compared to the alternative. This information is crucial to make decision regarding funding any new drug, diagnostic test or determining standard treatment protocol. It becomes even more important to consider this evidence in resource constrained low-income and middle-income country settings. Generating evidence on costs and consequences of a treatment or intervention could be performed in the setting of a randomized controlled trial, which is the perfect platform to evaluate efficacy or effectiveness. However, we argue that randomized controlled trial (RCT) offers an incomplete setting to generate comprehensive data on all costs and consequences for the purpose of a CEA. Hence, it is needed to use a decision model, either in combination with the evidence from RCT or alone. In this article, we demonstrate the application of decision model-based economic evaluation using 2 separate techniques - a decision tree and a Markov model. We argue that application of a decision model allows computation of health benefits in terms of utility-based measure such as a quality-adjusted life year or disability-adjusted life year which is preferred for a CEA, measure distal costs and consequences which are much more downstream to the application of intervention, allows comparison with multiple intervention and comparators, and provides opportunity of making use of evidence from multiple sources rather than a single RCT which may have limited generalizability. This makes the use of such evidence much more acceptable for clinical use and policy relevant.