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What is sensitivity analysis?
Matthew Taylor PhD MSc Senior Consultant, York Health Economics Consortium, University of York
G While economic models are a useful tool to aid decision-making in healthcare, there remain several types of uncertainty associated with this method of analysis. G One-way sensitivity analysis allows a reviewer to assess the impact that changes in a certain parameter will have on the model’s conclusions. G Sensitivity analysis can help the reviewer to determine which parameters are the key drivers of a model’s results. G By reporting extensive outputs from sensitivity analysis, modellers are able to consider a wide range of scenarios and, as such, can increase the level of confidence that a reviewer will have in the model. G Probabilistic sensitivity analysis provides a useful technique to quantify the level of confidence that a decision-maker has in the conclusions of an economic evaluation.
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Date of preparation: April 2009
What is sensitivity analysis?
The vast expansion in the number of available healthcare technologies, coupled with constraints placed on healthcare budgets, has increased the importance of decision-making with regards to new and existing health technologies. One method to determine the value for money of an intervention is to develop a ‘cost-effectiveness model’, predicting the costs and health outcomes that are likely to be associated with various different interventions. Models are a useful tool for representing the detailed and complex ‘real world’ with a more simple and understandable structure. While models do not claim to necessarily create an exact replica of the real world, they can be useful in demonstrating the relationships and interactions between various different factors. Furthermore, models allow the decision-maker to combine information from various sources and, in some cases, to extrapolate findings beyond the trial period.
One-way sensitivity analysis
The simplest form of sensitivity analysis is to simply vary one value in the model by a given amount, and examine the impact that the change has on the model’s results. For example, it might be shown that by changing the effectiveness of an intervention by 10%, the cost-effectiveness ratio falls by, say, 20%. This is known as one-way sensitivity analysis, since only one parameter is changed at one time. The analysis could, of course, be repeated on different parameters at different times. One-way sensitivity analysis can be undertaken using various different approaches, each of which is useful for different purposes. Suppose that a researcher would like to test which parameters have the greatest influence on a model’s results. In this case, each parameter in the model (or, at least, each of the key parameters) could be changed by a specific amount. Say, for example, that all parameters were to be increased and decreased by 20% of their original value. For each parameter change, the researcher might record the percentage impact on the model’s main outcome, which can be shown graphically in the form of a tornado diagram (Figure 1). While a tornado diagram is useful in demonstrating the impact that a fixed change in each parameter has on the main outcomes, it is not useful in representing the confidence that a decision-maker might have in the model’s inputs. For example, it might be that the level of confidence in one particular parameter is so low that it is entirely reasonable that the current input may be ‘wrong’ by as much as 100%. This might be seen in cases where no published data exist to support a particular model input. An example of this could be the impact of a drug on a patient’s long-term mortality, when only short-term trial data are available. Conversely, some...
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