Keynote Abstract:

A mathematical magic show – modelling when data are poor or non-existent

Steve Gallivan

Professor, Director of the Clinical Operational Research Unit
University College London, UK

Many applications of Operational Research in the context of health care, particularly those related to health economic evaluation, involve processes of calibration, validation and sensitivity analysis, usually relying to acquiring data or data summaries. Indeed these processes seem to have such an elevated status that their absence is often regarded as a marker that a study is somehow substandard. Undoubtedly this is may be the case; however there may be circumstances where it is perfectly reasonable not to use such methods. This paper concerns general principles underlying mathematical modelling particularly in contexts where data for calibration are either poor quality or non-existent. The discussion challenges the view that modelling should necessarily be subject to formulaic calibration, validation and sensitivity analysis processes in an attempt to achieve or establish ‘accuracy’. Some models are used purely to deduce the logical consequences of a set of beliefs and in this context; the need for validation is at best questionable. If calibration and sensitivity analysis are to be carried out, there is a need to be clear about what the objective is in such analyses. In some cases, such as modelling of cancer screening, a different form of analysis may be more appropriate based on examining pessimistic estimates of the benefits of health care options under consideration.