The points discussed in the previous sections are integral to the study of health economics and health economic evaluations which are a complex process that is carried out using health economic decision models. In order to formulate robust and effective decision models, there are specific model characteristic and aspects that should be considered. These aspects include time horizons, data presentation, sources of input data, comparators, model choices and the analytic perspective187,214.
2.4.2.1. Time
Timing is vital when it comes modelling for health economics, since the different time periods have an effect on the resolution and therefore the power of the information that the models produce. Time horizons must be chosen ad hoc, for instance for a rapidly developing illness, days or weeks would be an adequate measure to capture the information that would impact on the outcomes and costs of the intervention178,217. However, for a chronic illness, months and years would be more appropriate measure
as the outcomes of the intervention would be realised over a longer period of time, typically covering a natural life span188,210. Having such information would add value to the results obtained from the models
as payers would be able to extrapolate the long-term future costs and savings of different interventions. This information would be of greater value in comparison to the current models that have relatively short time horizons (one to five years), even for chronic illnesses.
2.4.2.2. Data presentation
Presentation of data and results is important for decision models and particularly useful for decision makers187,210. For instance, decision trees are convenient for CEA as they diagrammatically present the
consequences of two or more different options. This is because decision trees can display potential action options, their consequences and the resulting consequences measured in costs and health outcomes187. The cost-effectiveness plane is also a convenient tool as it allows for visual representation
of the ICER of the different interventions. Other useful presentations include one and two-way threshold analysis, ICER distribution graphs and cost-effectiveness acceptability curves (CEAC)187,215,218. Most
importantly with regards to data, is that it must be transparent, as this allows decision makers to truly understand or at least have a good basis of understanding of how the models have been conceptualised.
2.4.2.3. Data sources
Continuing with the topic of data, models should be data rich in terms of therapy effects and costs, particularly in different scenarios as this results in more informed and robust models which and can be used for effective and efficient decision making. There are various sources of data available, ranging from trial data and synthesised evidence through methods such as cohort simulation; it is at the discretion of analysts to choose appropriate data for their models213,219. When creating models caution
is necessary, as metrics that do not assume maximum utility are required, unless that is the realistic level to be obtained. This means that models that assume maximum utility need to be adjusted so that they are more representative of the actual effects of the therapy/product. Input data for CTPs presents a challenge as there are limited trials to provide information. Furthermore, due to the unconventional
nature of CTPs and their various mechanisms of action, traditional pharmaceutical data cannot be used as surrogate data.
2.4.2.4. Comparators
Since methods such as ICER cannot be performed in isolation, it is imperative to have comparators214,220. The gold standard and other effective interventions (as many as realistically possible)
should be compared in order to fully evaluate the true cost-effectiveness of the intervention188,205. If
relevant comparators are not included, the validity of the results could be brought into question as the cost-effectiveness analysis would not be fully representative of the interventions that are available, limiting the power of the information and consequently the reliability of the decisions made.
2.4.2.5. Model choice
Choosing the appropriate model is a key activity as the model should have the ability provide useful information relevant the decision that needs to be made. Key aspects to consider during model selection include defining the boundaries of the model and the availability of the data required to populate the model. Decision trees are useful for comparison of the different outcomes of two intervention options ( Figure 14). However, decision trees are limited in their scope as they are typically focussed on a single discrete time-period, therefore they cannot be effectively used to model long periods of time without becoming overly complicated and ‘bushy’187. In such circumstances, Markov models can be used as
they have the ability to model longer periods of time219. Markov models are based around stochastic
process and transition probabilities (Table 3 and Figure 15). Relating back to the importance of time horizons, Markov models present a useful tool to model long term treatment effects, which is imperative for interventions that target chronic diseases that would need to be modelled over an entire life span198,214.
Figure 14. An illustrative example of a decision tree to demonstrate how the effects of two different decision pathways can be traced to give an outcome. The decision node represented by the square shows the decision being made, the chance nodes (circles) branch out to the different pathways that result due to the decision made. The end nodes (triangles) represent the payoff of each pathway of each decision. The payoffs are calculated using probabilities of given parameters such as costs and health utilities. Therefore, a populated decision tree can be used to model the different costs associated with using different treatments as an intervention for a disease.
A
B
Effect 1
Effect 1
Effect 2
Effect 2
Figure 15. Markov models are based on stochastic processes which can be on a continuous timeframe. This allows them to be used for modelling the transition of an individual or cohort group from one state into another. The transitions are based on transition probabilities which can be weighted against health outcomes or costs, therefore Markov models can be used to model the costs and health outcomes related to a patient or group of patients being in a specific state or transitioning between states. In this illustrative example a patient can be asymptomatic of Parkinson’s disease(A), have moderate Parkinson’s disease(B), have severe Parkinson’s disease (C) or be in the absorbent state of death (D).
Death
No PD
Moderate
(>25% off time)
Severe
(<25% off time)
Asymptomatic (A)
Progressive (C)
Progressive (B)
Absorbent (D)
Table 3. Transition matrix showing the different transition probabilities (tp) from one state to another. This matrix works on the principle of staying in the same state or progressing to the next state, but not being able to go back into a previous state – as indicated by the direction of the arrows in Figure 15.
2.4.2.6. Perspective
Finally, it is important to understand the analytical perspective of the evaluations being carried out. All parties, from a moral point of view, should have the health outcomes and effectiveness as the priority of their intervention187,214. However, monetary considerations are important as budgets are generally
limited, expectedly developers and payers will have different perspectives. For instance, developers will want to be granted the highest possible reimbursement price whilst payers will want the lowest reimbursement price for the most effective intervention. Patients are also another stakeholder, their perspective (particularly in national healthcare systems) is to have access to the most effective interventions136,187,191. With all these different and at times conflicting stakeholder perspectives to
consider, models need to be as informative and transparent as possible so that all parties are able to infer relevant information for their decision-making processes.