Updating risk prediction tools: A case study in prostate cancer

Donna P. Ankerst, Tim Koniarski, Yuanyuan Liang, Robin J. Leach, Ziding Feng, Martin G. Sanda, Alan W. Partin, Daniel W. Chan, Jacob Kagan, Lori Sokoll, John T. Wei, Ian M. Thompson

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

27 Zitate (Scopus)


Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network.

Seiten (von - bis)127-142
FachzeitschriftBiometrical Journal
PublikationsstatusVeröffentlicht - Jan. 2012


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