Abstract

Letters2 April 2013Predictive Accuracy of the Liverpool Lung Project Risk ModelJohn K. Field, PhD, Olaide Y. Raji, PhD, and Stephen W. Duffy, MScJohn K. Field, PhDFrom The University of Liverpool Cancer Research Centre, Institute of Translational Medicine, The University of Liverpool, Liverpool, and Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.Search for more papers by this author, Olaide Y. Raji, PhDFrom The University of Liverpool Cancer Research Centre, Institute of Translational Medicine, The University of Liverpool, Liverpool, and Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.Search for more papers by this author, and Stephen W. Duffy, MScFrom The University of Liverpool Cancer Research Centre, Institute of Translational Medicine, The University of Liverpool, Liverpool, and Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-158-7-201304020-00015 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We thank Dr. Young and colleagues for their interest and useful comments on our article. The LLP risk model was shown to perform better than those based on age and smoking history alone. The superior net benefit shown in 3 independent studies suggested a predicted clinical utility in terms of screening decision at relevant thresholds; however, this was a retrospective performance assessment and the actual effect can only be shown with prospective CT screening trials.The detection rate of 1.5% that Baldwin and associates (1) reported in 2011 was a conservative estimate used to power the UKLS (United ...

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