Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies
| Autor: | Pennells, Lisa, Kaptoge, Stephen, White, Ian R., Thompson, Simon G., Wood, Angela M., Tipping, Robert W., Folsom, Aaron R., Couper, David J., Ballantyne, Christie M., Coresh, Josef, Goya Wannamethee, S., Morris, Richard W., Kiechl, Stefan, Willeit, Johann, Willeit, Peter, Schett, Georg, Ebrahim, Shah, Lawlor, Debbie A., Yarnell, John W., Gallacher, John, Cushman, Mary, Psaty, Bruce M., Tracy, Russ, Tybjærg-Hansen, Anne, Price, Jackie F., Lee, Amanda J., McLachlan, Stela, Khaw, Kay-Tee, Wareham, Nicholas J., Brenner, Hermann, Schöttker, Ben, Müller, Heiko, Jansson, Jan-Håkan, Wennberg, Patrik, Salomaa, Veikko, Harald, Kennet, Jousilahti, Pekka, Vartiainen, Erkki, Woodward, Mark, D'Agostino, Ralph B., Bladbjerg, Else-Marie, Jørgensen, Torben, Kiyohara, Yutaka, Arima, Hisatomi, Doi, Yasufumi, Ninomiya, Toshiharu, Dekker, Jacqueline M., Nijpels, Giel, Stehouwer, Coen D. A., Kauhanen, Jussi, Salonen, Jukka T., Meade, Tom W., Cooper, Jackie A., Shea, Steven, Döring, Angela, Kuller, Lewis H., Grandits, Greg, Gillum, Richard F., Mussolino, Michael, Rimm, Eric B., Hankinson, Sue E., Manson, JoAnn E., Pai, Jennifer K., Kirkland, Susan, Shaffer, Jonathan A., Shimbo, Daichi, Bakker, Stephan J. L., Gansevoort, Ron T., Hillege, Hans L., Amouyel, Philippe, Arveiler, Dominique, Evans, Alun, Ferrières, Jean, Sattar, Naveed, Westendorp, Rudi G., Buckley, Brendan M., Cantin, Bernard, Lamarche, Benoît, Barrett-Connor, Elizabeth, Wingard, Deborah L., Bettencourt, Richele, Gudnason, Vilmundur, Aspelund, Thor, Sigurdsson, Gunnar, Thorsson, Bolli, Kavousi, Maryam, Witteman, Jacqueline C., Hofman, Albert, Franco, Oscar H., Howard, Barbara V., Zhang, Ying, Best, Lyle, Umans, Jason G., Onat, Altan, Sundström, Johan, Michael Gaziano, J., Stampfer, Meir, Ridker, Paul M., Marmot, Michael, Clarke, Robert, Collins, Rory, Fletcher, Astrid, Brunner, Eric, Shipley, Martin, Kivimäki, Mika, Buring, Julie, Cook, Nancy, Ford, Ian, Shepherd, James, Cobbe, Stuart M., Robertson, Michele, Walker, Matthew, Watson, Sarah, Alexander, Myriam, Butterworth, Adam S., Angelantonio, Emanuele Di, Gao, Pei, Haycock, Philip, Wormser, David, Danesh, John |
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| Přispěvatelé: | MUMC+: HVC Pieken Maastricht Studie (9), Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), RS: CARIM - R3 - Vascular biology |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Male
Epidemiology Practice of Epidemiology Poison control Coronary Disease Weighting Inverse variance Risk Assessment risk prediction Meta-Analysis as Topic Risk Factors Statistics Medicine Humans Prospective Studies coronary heart disease 10. No inequality Prospective cohort study Survival analysis Proportional Hazards Models Models Statistical business.industry Proportional hazards model C index Individual participant data inverse variance individual participant data Middle Aged D measure Risk prediction Coronary heart disease meta-analysis Meta-analysis C-Reactive Protein Data Interpretation Statistical weighting Female business Risk assessment |
| Zdroj: | American Journal of Epidemiology, 179(5), 621-632. Oxford University Press American Journal of Epidemiology Am. J. Epidemiol. 179, 621-632 (2014) the Emerging Risk Factors Collaboration 2014, ' Assessing risk prediction models using individual participant data from multiple studies ', American Journal of Epidemiology, vol. 179, no. 5, pp. 621-632 . https://doi.org/10.1093/aje/kwt298 Bladbjerg, E-M, Jespersen, J & Emerging Risk Factors Collaboration 2014, ' Assessing risk prediction models using individual participant data from multiple studies ', American Journal of Epidemiology, vol. 179, no. 5, pp. 621-632 . https://doi.org/10.1093/aje/kwt298 |
| ISSN: | 0002-9262 |
| Popis: | 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 a weighted 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 case-control 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. |
| Databáze: | OpenAIRE |
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