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Estimation with External Information

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Clinical Prediction Models

Part of the book series: Statistics for Biology and Health ((SBH))

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Abstract

In this chapter, we discuss methods that estimate regression coefficients based on the combination of findings in the sample under study with external information from published studies. The aim is to develop a global model, which has broad applicability. Such a model might be informed by a meta-analysis based on individual patient data (IPD) from multiple studies. We illustrate this approach with a meta-analysis of 15 studies of traumatic brain injury. Another aim is to obtain a better model for a specific, local setting. This aim may be addressed by a simple “adaptation” method for univariate regression coefficients, which are obtained from a meta-analysis. We illustrate this method in a case study of operative mortality of abdominal aneurysm surgery. We discuss some further approaches to estimation of regression coefficients, including stacked regressions and Bayesian estimation with explicit prior information.

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Correspondence to Ewout W. Steyerberg PhD .

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Steyerberg, E.W. (2019). Estimation with External Information. In: Clinical Prediction Models. Statistics for Biology and Health. Springer, Cham. https://doi.org/10.1007/978-3-030-16399-0_14

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