Strategies for handling missing data in the development, validation and implementation of clinical risk prediction tools

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Tools that predict the future outcome of disease are common. Missing data is a problem in studies that develop and validate such tools and affects their validity because simple approaches to dealing with missing data are biased. We will develop statistical methodology in this area and compare the performance of this and other methodologies. Alongside this methodological work we will re-assess existing prediction tools and develop new tools in the areas of cardiac surgery and kidney disease.