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.
Přispěvatelé: Interne Geneeskunde, RS: CARIM School for Cardiovascular Diseases
Jazyk: angličtina
Rok vydání: 2012
Předmět:
Nutrition and Disease
Epidemiology
Cost effectiveness
Cost-Benefit Analysis
cardiovascular-disease
Kaplan-Meier Estimate
01 natural sciences
Health informatics
010104 statistics & probability
0302 clinical medicine
cardiovascular disease
Voeding en Ziekte
Econometrics
Medicine
030212 general & internal medicine
Research Articles
competing risks
validation
Framingham Risk Score
Cost–benefit analysis
coronary heart-disease
Discriminant Analysis
cohort
Prognosis
3. Good health
Cardiovascular Diseases
Meta-analysis
Risk assessment
metaanalysis
Statistics and Probability
reclassification
Context (language use)
risk score
Risk Assessment
screening strategies
statins
03 medical and health sciences
Meta-Analysis as Topic
Humans
0101 mathematics
cost-effectiveness
Proportional Hazards Models
VLAG
business.industry
Proportional hazards model
Cardiovascular disease
Competing risks
Cost-effectiveness
Net benefit
Screening strategies
Epidemiologic Research Design
predictive ability
R1
meta-analysis
roc curve
business
net benefit
Zdroj: 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
ISSN: 0277-6715
DOI: 10.1002/sim.4362
Popis: 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. Copyright © 2011 John Wiley & Sons, Ltd.
Databáze: OpenAIRE
Externí odkaz: