Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Assessing risk prediction models using individual participant data from multiple studies|
Goya Wannamethee, S.
|Citation:||American Journal of Epidemiology, 2014; 179(5):621-632|
|Publisher:||Oxford University Press|
|Lisa Pennells, Stephen Kaptoge, Ian R. White, Simon G. Thompson, Angela M. Wood and the Emerging Risk Factors Collaboration (Debbie A. Lawlor)|
|Abstract:||Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied).We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell’s concordance index, and Royston’s discrimination measure within each study; we then combine the estimates across studies using aweighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from casecontrol studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.|
|Keywords:||C index; coronary heart disease; D measure; individual participant data; inverse variance; meta-analysis; risk prediction; weighting|
|Rights:||© The Author 2013. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.|
|Appears in Collections:||Medicine publications|
Files in This Item:
|hdl_104634.pdf||Published version||626.48 kB||Adobe PDF||View/Open|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.