Decision analysis

Decision analysis has been used in different settings such as marketing, law and engineering, but is also increasingly and successfully used in the evaluation of healthcare interventions (Gray et al., 2011). Decision analysis allows health economists to examine the costs and effects of theoretical choices made about a healthcare interventions, without the corresponding (and potentially negative) consequences occurring in the real world. Data to

inform such theoretical decision models can be included from a variety of sources, and a number of alternative choices and their consequences can be modelled. As most research data includes a degree of uncertainty (usually expressed in confidence intervals or interquartile ranges), decision analysis models can include ranges of costs and effects of an intervention in the analysis. This leads to a more realistic overall estimate of cost- effectiveness.

Decision tree models are frequently used in one or more of the following circumstances alongside studies of clinical effects; adapted from Gray et al. (2011):

 An RCT of the intervention of interest is not feasible.

 The research does not compare all the relevant alternatives.  Information from an number of studies has to be combined.  The study does not include relevant long-term outcomes.

As prehospital research is frequently subject to barriers and limitations, combining evidence from multiple sources in a decision analysis model can be seen as a pragmatic and goal- oriented approach (Sun and Faunce, 2008).

In the context of prehospital critical care for OHCA, funding decisions have been made, and continue to be made, across the UK by NHS ambulance services and air ambulance charities (von Vopelius-Feldt and Benger, 2014a; Hyde et al., 2012). In Chapter 5, I described how a lack of data on the cost-effectiveness of prehospital critical care for OHCA results in stakeholders’ opposing views on funding such services. It is worth stating explicitly that continuing the status-quo of funding or not funding a service due to a current lack of evidence (rather than evidenced lack of benefit) are both decisions of potential magnitude and associated opportunity costs (Gray et al., 2011). Any evidence to guide these decisions is therefore of great value. The aim of the decision modelling analysis is to provide stakeholders in prehospital critical care with an estimation of its cost-effectiveness following OHCA, acknowledging the uncertainty of this estimation, while drawing from the best available evidence.

Decision modelling is one of many approaches available to health economists and possesses distinct advantages and limitations which need to be considered in the planning, execution and interpretation of the economic analysis (Gray et al., 2011). The main advantage in regards to this thesis is the pragmatic approach of utilising a number of potential sources of data to support the analysis, as described in Box 6.1.

Box 6.1. Steps of decision analysis modelling, adapted from Philips et al. (2006) and Briggs, Claxton and Sculpher (2006)

Step 1 – Define the question to be addressed by the economic analysis.

This includes the clinical setting, the range of alternatives being examined and the time horizon and perspective of the economic analysis. The boundaries of the model need to be clearly specified.

Step 2 - Select the appropriate analytical model.

The most common models used in health economics are the decision tree model and Markov model, other options include individual sampling model, systems dynamic model or discrete event simulation.

Step 3 – Create the model structure.

The model structure should include all relevant clinical events, effects of interventions and outcomes. Trade-off decisions between complex and pragmatic modelling are frequently required.

Step 4 – Identify and synthesise relevant evidence.

Potential sources are own research data, previous published studies or meta-analyses. This can be supplemented by reports from governments or other institutions. The Office of National Statistics’ Life Tables (Office for National Statistics, 2016) or Department of Health’s reference costs (Department of Health, 2016a) are frequently used in economic analyses the UK. If expert opinion is utilised, this needs to be clearly stated.

Step 5 – Refine and critically review the model.

The model might need to be adjusted in order to reflect and incorporate evidence identified during the previous step. Consideration should be given to external and internal validity of the model.

Step 6 – Fit parameter values and distributions.

Estimates and distributions for each parameters, informed by the data synthesis in Step 4, are fitted to the model. Care should be taken to consider appropriate distributions for different types of parameters.

Step 7 – Run a probabalistic sensitivity analysis and relevant one-way sensitivity analyses.

To reflect the uncertainty in the underlying data and to asess its impact on the estimate of cost-effectiveness of the intervention, probabalistic sensitivity analysis is undertaken. Parameters of particular interest to decision makers can be manipulated directly and assessed in more detail through a focused sensitivity analysis.

Step 8 – Present the results of the decision modelling analysis.

Results can be displayed in a variety of ways. Commonly used are incremental cost- effectiveness ratios (ICERs), the cost-effectiveness plane or cost-effectiveness acceptability curves (CEACs).

On the other hand, decision analysis modelling can be criticised for being a theoretical construct of reality, with potentially significant limitations as to how accurately the real world

is reflected in the model (Briggs, Claxton and Sculpher, 2006). It is therefore important that the process of decision modelling is explicit and described in a transparent and logical manner, which I will attempt in the following sections. Particular focus will be placed on the process of information synthesis. To create the most accurate model, I will draw on my own research but also various other sources of published data.

Finally, a strength of decision analysis is the ability to reflect the uncertainty of the data on which the model was built. This will be achieved through a probabilistic sensitivity analysis, where each parameter in the model is not only assigned a single estimate value but its value is drawn from a distribution of possible values. A Monte Carlo simulation will draw random values for all parameters in the model during repeated analysis of the modelled intervention’s costs and effects. The result will be a probability of cost-effectiveness of prehospital critical care for various willingness-to-pay thresholds. The decision analysis modelling in this chapter was undertaken following a step-wise approach adapted from Briggs, Claxton and Sculpher (2006) and Philips et al. (2006); see Box 6.1.