5. Summing it all up: Synthesis of clinical research

Systematic reviews coupled with meta-analysis can provide more precise estimates of the relative impact of various interventions. But how can this information be used to support decision - making? Simulation modelling is a technique that allows the combination of different types of information (clinical, epidemiological, economical, etc.) for an overall picture of the relative costs and effectiveness of medical treatment.

Simulation models are not constrained to a single type of information or a single outcome. These models are useful, particularly when a lengthy wait for more evidence is not feasible. At their core, simulation models combine probabilities to obtain estimates of expected values. These values can be either clinical outcomes, or costs, making simulation modelling useful for economic evaluation.

These models require assumptions to be inserted regarding various outcomes, which ultimately influence the endpoints calculated by a model. The accuracy and reliability of these assumptions will vary according to the treatment in question and the quantity and quality of evidence that supports the selection of the value inserted for each ‘assumption’. Many HTA guidelines provide specific guidance on selecting and supporting those model ‘inputs’ to assist reviewers in making judgements regarding the overall quality and ‘probability’ of the model’s predictions.