A framework for quantifying net benefits of alternative prognostic models

Autor: Rapsomaniki, E., White, I. R., Wood, A. M., Thompson, S. G., Tipping, R. W., Ford, C. E., Simpson, L. M., Folsom, A. R., Chambless, L. E., Panagiotakos, D. B., Pitsavos, C., Chrysohoou, C., Stefanadis, C., Knuiman, M., Whincup, P. H., Wannamethee, S. G., Morris, R. W., Kiechl, S., Willeit, J., Oberhollenzer, F., Mayr, A., Wald, N., Lawlor, D. A., Yarnell, J. W., Gallacher, J., Casiglia, E., Tikhonoff, V., Nietert, P. J., Sutherland, S. E., Bachman, D. L., Keil, J. E., Cushman, M., Tracy, R., Tybjaerg-Hansen, A., Nordestgaard, B. G., Frikke-Schmidt, R., Giampaoli, S., Palmieri, L., Panico, S., Vanuzzo, D., Pilotto, L., Gomez de la Camara, A., Gomez Gerique, J. A., Simons, L., Mccallum, J., Friedlander, Y., Lee, A. J., Taylor, J., Guralnik, J. M., Wallace, R., Blazer, D. G., Khaw, K. -T., Schottker, B., Muller, H., Rothenbacher, D., Jansson, J. -H., Wennberg, P., Nissinen, A., Donfrancesco, C., Salomaa, V., Harald, K., Jousilahti, P., Vartiainen, E., Woodward, M., D'Agostino Sr, R. B., Wolf, P. A., Vasan, R. S., Pencina, M. J., Bladbjerg, E. -M., Jorgensen, T., Moller, L., Jespersen, J., Dankner, R., Chetrit, A., Lubin, F., Rosengren, A., Lappas, G., Eriksson, H., Bjorkelund, C., Lissner, L., Bengtsson, C., Nagel, D., Kiyohara, Y., Arima, H., Doi, Y., Ninomiya, T., Rodriguez, B., Dekker, J. M., Nijpels, G., Stehouwer, C. D. A., Iso, H., Kitamura, A., Yamagishi, K., Noda, H., Goldbourt, U., Kauhanen, J., Salonen, J. T., Cooper, J. A., Verschuren, W. M. M., Blokstra, A., Shea, S., Doring, A., Meisinger, C., Bueno-de-Mesquita, H. B., Kuller, L. H., Grandits, G., Gillum, R. F., Mussolino, M., Bauer, K. A., Kirkland, S., Shaffer, J., Korin, M. R., Sato, S., Amouyel, P., Arveiler, D., Evans, A., Ferrieres, J., Schulte, H., Assmann, G., Westendorp, R. G., Buckley, B. M., Packard, C. J., Sattar, N., Cantin, B., Despres, J. -P., Dagenais, G. R., Barrett-Connor, E., Wingard, D. L., Bettencourt, R., Gudnason, V., Aspelund, T., Sigurdsson, G., Thorsson, B., Witteman, J., Kardys, I., Tiemeier, H., Hofman, A., Tunstall-Pedoe, H., Tavendale, R., Lowe, G. D. O., Howard, B. V., Zhang, Y., Best, L., Umans, J., Ben-Shlomo, Y., Davey-Smith, G., Njolstad, I., Wilsgaard, T., Ingelsson, E., Lind, L., Giedraitis, V., Lannfelt, L., Gaziano, J. M., Stampfer, M., Ridker, P., Wassertheil-Smoller, S., Manson, J. E., Marmot, M., Clarke, R., Fletcher, A., Brunner, E., Shipley, M., Buring, J., Shepherd, J., Cobbe, S. M., Ford, I., Robertson, M., Marin Ibanez, A., Feskens, E. J. M., Kromhout, D.

Publikováno v: Statistics in Medicine, 31(2), 114-130
Statistics in Medicine
Statistics in Medicine 31 (2012) 2
Statistics in Medicine, 31(2), 114-130. John Wiley & Sons Inc.
Rapsomaniki, E, White, I R, Wood, A M, Thompson, S G, Emerging Risk Factors Collaboration, Bladbjerg, E-M & Jespersen, J 2012, ' A framework for quantifying net benefits of alternative prognostic models ', Statistics in Medicine, vol. 31, no. 2, pp. 114-30 . https://doi.org/10.1002/sim.4362

New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by qu

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https://research.wur.nl/en/publications/a-framework-for-quantifying-net-benefits-of-alternative-prognosti