A framework for quantifying net benefits of alternative prognostic models

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

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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 quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with the multistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robust against a range of modelling assumptions, including adjusting for competing risks.

Original languageEnglish (US)
Pages (from-to)114-130
Number of pages17
JournalStatistics in Medicine
Issue number2
StatePublished - Jan 30 2012
Externally publishedYes


  • Cardiovascular disease
  • Competing risks
  • Cost-effectiveness
  • Meta-analysis
  • Net benefit
  • Screening strategies

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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