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1. Sex-specific relevance of diabetes to occlusive vascular and other mortality: a collaborative meta-analysis of individual data from 980 793 adults from 68 prospective studies

Author

Gnatiuc, L, Herrington, WG, Halsey, J, Tuomilehto, J, Fang, X, Kim, HC, De Bacquer, D, Dobson, AJ, Criqui, MH, Jacobs, DR, Jr, Leon, DA, Peters, SAE, Ueshima, H, Sherliker, P, Peto, R, Collins, R, Huxley, RR, Emberson, JR, Woodward, M, Lewington, S, Aoki, N, Arima, H, Arnesen, E, Aromaa, A, Assmann, G, Bachman, DL, Baigent, C, Bartholomew, H, Benetos, A, Bengtsson, C, Bennett, D, Björkelund, C, Blackburn, H, Bonaa, K, Boyle, E, Broadhurst, R, Carstensen, J, Chambless, L, Chen, Z, Chew, SK, Clarke, R, Cox, C, Curb, JD, D'Agostino, R, Date, C, Davey Smith, G, De Backer, G, Dhaliwal, SS, Duan, XF, Ducimetiere, P, Duffy, S, Eliassen, H, Elwood, P, Empana, J, Garcia-Palmieri, MH, Gazes, P, Giles, GG, Gillis, C, Goldbourt, U, Gu, DF, Guasch-Ferre, M, Guize, L, Haheim, L, Hart, C, Hashimoto, S, Hashimoto, T, Heng, D, Hjermann, I, Ho, SC, Hobbs, M, Hole, D, Holme, I, Horibe, H, Hozawa, A, Hu, F, Hughes, K, Iida, M, Imai, K, Imai, Y, Iso, H, Jackson, R, Jamrozik, K, Jee, SH, Jensen, G, Jiang, CQ, Johansen, NB, Jorgensen, T, Jousilahti, P, Kagaya, M, Keil, J, Keller, J, Kim, IS, Kita, Y, Kitamura, A, Kiyohara, Y, Knekt, P, Knuiman, M, Kornitzer, M, Kromhout, D, Kronmal, R, Lam, TH, Law, M, Lee, J, Leren, P, Levy, D, Li, YH, Lissner, L, Luepker, R, Luszcz, M, MacMahon, S, Maegawa, H, Marmot, M, Matsutani, Y, Meade, T, Morris, J, Morris, R, Murayama, T, Naito, Y, Nakachi, K, Nakamura, M, Nakayama, T, Neaton, J, Nietert, PJ, Nishimoto, Y, Norton, R, Nozaki, A, Ohkubo, T, Okayama, A, Pan, WH, Puska, P, Qizilbash, N, Reunanen, A, Rimm, E, Rodgers, A, Saitoh, S, Sakata, K, Sato, S, Schnohr, P, Schulte, H, Selmer, R, Sharp, D, Shifu, X, Shimamoto, K, Shipley, M, Silbershatz, H, Sorlie, p, Sritara, P, Suh, I, Sutherland, SE, Sweetnam, P, Tamakoshi, A, Tanaka, H, Thomsen, T, Tominaga, S, Tomita, M, Törnberg, S, Tunstall-Pedoe, H, Tverdal, A, Vartiainen, E, Wald, N, Wannamethee, SG, Welborn, TA, Whincup, P, Whitlock, G, Willett, W, Woo, J, Wu, ZL, Yao, SX, Yarnell, J, Yokoyama, T, Yoshiike, N, and Zhang, XH

Published
2018
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36. Covariate-adjusted measures of discrimination for survival data

Author

White, Ian R, Rapsomaniki, Eleni, Wannamethee, S. G., Morris, R. W., Willeit, J., Willeit, P., Santer, P., Kiechl, S., Wald, N., Ebrahim, S., Lawlor, D. A., Gallacher, J., Yarnell, J. W. G., Ben Shlomo, Y., Casiglia, Edoardo, Tikhonoff, V., Sutherland, S. E., Nietert, P. J., Keil, J. E., Bachman, D. L., Psaty, B. M., Cushman, M., Nordestgaard, B. G., Tybjærg Hansen, A., Frikke Schmidt, R., Giampaoli, S., Palmieri, L., Panico, S., Pilotto, L., Vanuzzo, D., Simons, L. A., Friedlander, Y., Mccallum, J., Price, J. F., Mclachlan, S., Taylor, J. O., Guralnik, J. M., Wallace, R. B., Kohout, F. J., Cornoni Huntley, J. C., Blazer, D. G., Phillips, C. L., Wareham, N. J., Khaw, K. T., Brenner, H., Schöttker, B., Müller, H. T., Rothenbacher, D., Nissinen, A., Donfrancesco, C., Harald, K., Jousilahti, P. R., Vartiainen, E., Salomaa, V., D'Agostino, R. B., Wolf, P. A., Vasan, R. S., Daimon, M., Oizumi, T., Kayama, T., Kato, T., Chetrit, A., Dankner, R., Lubin, F., Welin, L., Svärdsudd, K., Eriksson, H., Lappas, G., Lissner, L., Mehlig, K., Björkelund, C., Nagel, D., Kiyohara, Y., Arima, H., Ninomiya, T., Hata, J., 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., Tuomainen, T. P., Meade, T. W., Destavola, B. L., Blokstra, A., Verschuren, W. M. M., de Boer, I. H., Folsom, A. R., Koenig, W., Meisinger, C., Peters, A., Bueno de Mesquita, H. B., Rosengren, A., Wilhelmsen, L., Kuller, L. H., Grandits, G., Cooper, J. A., Bauer, K. A., Davidson, K. W., Kirkland, S., Shaffer, J. A., Shimbo, D., Sato, S., Dullaart, R. P. F., Bakker, S. J. L., Gansevoort, R. T., Ducimetiere, P., Amouyel, P., Arveiler, D., Evans, A., Ferrières, J., Schulte, H., Assmann, G., Jukema, J. W., Westendorp, R. G. J., Sattar, N., Cantin, B., Lamarche, B., Després, J. P., Null, E. Barrett Connor, Wingard, D. L., Daniels, L. B., Gudnason, V., Aspelund, T., Trevisan, M., Hofman, A., Franco, O. H., Tunstall Pedoe, H., Tavendale, R., Lowe, G. D. O., Woodward, M., Howard, W. J., Howard, B. V., Zhang, Y., Best, L. G., Umans, J., Davey Smith, G., Onat, A., Nakagawa, H., Sakurai, M., Nakamura, K., Morikawa, Y., Njølstad, I., Mathiesen, E. B., Wilsgaard, T., Sundström, J., Gaziano, J. M., Ridker, P. M., Marmot, M., Clarke, R., Collins, R., Fletcher, A., Brunner, E., Shipley, M., Kivimaki, M., Buring, J., Rifai, N., Cook, N., Ford, I., Robertson, M., Marín Ibañez, A., Feskens, E. J. M., Geleijnse, J. M., MUMC+: HVC Pieken Maastricht Studie (9), Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), RS: FHML non-thematic output, Apollo - University of Cambridge Repository, White, Ian R, Rapsomaniki, Eleni, and Panico, Salvatore

Subjects
Statistics and Probability, Male, Biometry, C-index, D-index, Discrimination, Analysis of Variance, Cardiovascular Diseases, Clinical Trials as Topic, Discriminant Analysis, Female, Humans, Middle Aged, Risk Factors, Survival Analysis, Medicine (all), Statistics, Probability and Uncertainty, 01 natural sciences, Article, Unit (housing), 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Survival data, Cardiovascular Disease, Covariate, Statistics, 030212 general & internal medicine, 0101 mathematics, 10. No inequality, Prognostic models, Survival analysis, Medicine(all), Proportional hazards model, Risk Factor, General Medicine, Probability and Uncertainty, Discriminant Analysi, Demography, Human
Abstract

Discrimination statistics describe the ability of a survival model to assign higher risks to individuals who experience earlier events: examples are Harrell's C-index and Royston and Sauerbrei's D, which we call the D-index. Prognostic covariates whose distributions are controlled by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination statistics in censored survival data.To develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s).We define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration.The proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were substantially smaller than unadjusted values. The study-specific standard deviation of baseline age was strongly associated with the unadjusted C-index and D-index but not significantly associated with the age-adjusted indices.The proposed estimators improve meta-analysis comparisons, are easy to implement and give a more meaningful clinical interpretation.? 2014 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Published
2015
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