Classification rules for identifying individuals at high risk of developing myocardial infarction based on ApoB, ApoA1 and the ratio were determined using a Bayesian approach

References

• H. Akaike, A new look at the statistical model identification, IEEE Trans. Automat. Control 19 (1974), pp. 716–723. doi: 10.1109/TAC.1974.1100705
   Web of Science ®Google Scholar
• S.P.B. Brooks and A.G. Gelman, General methods for monitoring convergence of iterative simulations, J. Comput. Graph. Statist. 7 (1998), pp. 434–455.
   Web of Science ®Google Scholar
• A. Gelman and D.B. Rubin, Inference from iterative simulation using multiple sequences, Statist. Sci. 7 (1992), pp. 457–511. doi: 10.1214/ss/1177011136
   Google Scholar
• J. Geweke, Evaluating the accuracy of sampling-based approaches to calculating posterior moments, in Bayesian Statistics (Vol. 4), J.M. Bernado, J.O. Berger, A.P. David, and A.F.M. Smith, eds., Oxford University Press, Oxford, 1992, pp. 1–31.
   Google Scholar
• P. Heidelberger and P.D. Welch, Simulation run length control in the presence of an initial transient, Oper. Res. 31 (1983), pp. 1109–1144. doi: 10.1287/opre.31.6.1109
   Web of Science ®Google Scholar
• I. Holme, A.H. Aastveit, I. Jungner, and G. Walldius, Relationships between lipoprotein components and risk of myocardial infarction: age, gender and short versus longer follow-up periods in the Apolipoprotein MOrtality RISk study (AMORIS), J. Intern. Med. 264 (2008), pp. 30–38. Available at http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2796.2008.02016.x/epdf. doi: 10.1111/j.1365-2796.2008.01925.x
   PubMed Web of Science ®Google Scholar
• D.W. Hosmer, S. Lemeshow, and R.X. Sturdivant, Applied Logistic Regression, 3rd ed., Wiley-Interscience, New York, 2013. Available at http://ca.wiley.com/WileyCDA/WileyTitle/productCd-0470582472.html.
   Google Scholar
• L. Lind, B. Vessby, and J. Sundstro, The apolipoprotein B / AI ratio and the metabolic syndrome independently predict risk for myocardial infarction in middle-aged men, Arterioscler. Thromb. Vasc. Biol. 26 (2006), pp. 406–410. doi: 10.1161/01.ATV.0000197827.12431.d0
   PubMed Web of Science ®Google Scholar
• M.J. McQueen, S. Hawken, X. Wang, S. Ounpuu, A. Sniderman, J. Probstfield, K. Steyn, J.E. Sanderson, M. Hasani, E. Volkova, K. Kazmi, and S. Yusuf, Lipids, lipoproteins, and apolipoproteins as risk markers of myocardial infarction in 52 countries (the INTERHEART study): A case-control study, Lancet 372 (2008), pp. 224–233. doi: 10.1016/S0140-6736(08)61076-4
   PubMed Web of Science ®Google Scholar
• I. Ntzoufras, Bayesian Modeling Using WinBUGS, 2nd ed., John Wiley & Sons, Inc., Hoboken, NJ, 2009.
   Google Scholar
• S. Parish, R. Peto, A. Palmer, R. Clarke, S. Lewington, A. Offer, G. Whitlock, S. Clark, L. Youngman, P. Sleight, and R. Collins, The joint effects of apolipoprotein B, apolipoprotein A1, LDL cholesterol, and HDL cholesterol on risk: 3510 cases of acute myocardial infarction and 9805 controls, Eur. Heart J. 30 (2009), pp. 2137–46. Available at http://eurheartj.oxfordjournals.org/content/30/17/2137. doi: 10.1093/eurheartj/ehp221
   PubMed Web of Science ®Google Scholar
• M.S. Pepe, The Statistical Evaluation of Medical Test for Classification and Prediction, Oxford University Press, Oxford, 2003.
   Google Scholar
• R Development Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2011. ISBN 3-900051-07-0, 2011. Available at http://www.r-project.org/.
   Google Scholar
• A.P. Sabino, M. De Oliveira Sousa, L. Moreira Lima, D. Dias Ribeiro, L.M. Sant'Ana Dusse, M. DasGraças Carvalho, and A.P. Fernandes, ApoB/ApoA-I ratio in young patients with ischemic cerebral stroke or peripheral arterial disease, Trans. Res. 152 (2008), pp. 113–118. Available at http://www.ncbi.nlm.nih.gov/pubmed/18774540. doi: 10.1016/j.trsl.2008.06.005
   Web of Science ®Google Scholar
• B.J. Smith, Boa: An R package for MCMC output convergence, J. Statist. Softw. 21 (2007), pp. 1–37. Available at http://www.jstatsoft.org/v21/i11/paper?pagewanted=all. doi: 10.18637/jss.v021.i11
   Web of Science ®Google Scholar
• A.D. Sniderman, I. Jungner, I. Holme, A. Aastveit, and G. Walldius, Errors that result from using the TC/HDL C ratio rather than the apoB/apoA-I ratio to identify the lipoprotein-related risk of vascular disease, J. Intern. Med. 259 (2006), pp. 455–461. Available at http://www.ncbi.nlm.nih.gov/pubmed/16629851. doi: 10.1111/j.1365-2796.2006.01649.x
   PubMed Web of Science ®Google Scholar
• D.J. Spiegelhalter, N.G. Best, B.P. Carlin, and A. van der Linde, Bayesian measures of model complexity and fit, J. R. Stat. Soc. Ser. B 64 (2002), pp. 583–639. doi: 10.1111/1467-9868.00353
   Google Scholar
• D. Spiegelhalter, A. Thomas, N. Best, and R. Way, WinBUGS User Manual Version 1.4. MRC Biostatistics Unit, Cambridge, 2003. Available at http://www.mrc-bsu.cam.ac.uk/wp-content/uploads/manual14.pdf.
   Google Scholar
• S. Sturtz, U. Ligges, and A. Gelman, R2WinBUGS: A package for running WinBUGS from R, J. Statist. Softw. 12 (2005), pp. 1–16. doi: 10.18637/jss.v012.i03
   Web of Science ®Google Scholar
• A. Thompson and J. Danesh, Associations between apolipoprotein B, apolipoprotein AI, the apolipoprotein B/AI ratio and coronary heart disease: A literature-based meta-analysis of prospective studies, J. Intern. Med. 259 (2006), pp. 481–492. doi: 10.1111/j.1365-2796.2006.01644.x
   PubMed Web of Science ®Google Scholar
• G. Walldius, A.H. Aastveit, and I. Jungner, Stroke mortality and the apoB/apoA-I ratio: Results of the AMORIS prospective study, J. Intern. Med. 259 (2006), pp. 259–266. Available at http://www.ncbi.nlm.nih.gov/pubmed/16476103. doi: 10.1111/j.1365-2796.2005.01610.x
   PubMed Web of Science ®Google Scholar
• G. Walldius, I. Jungner, A.H. Aastveit, I. Holme, C.D. Furberg, and A.D. Sniderman, The apoB/apoA-I ratio is better than the cholesterol ratios to estimate the balance between plasma proatherogenic and antiatherogenic lipoproteins and to predict coronary risk, Clin. Chem. Lab. Med. 42 (2004), pp. 1355–1363. doi: 10.1515/CCLM.2004.254
   PubMed Web of Science ®Google Scholar
• S. Yusuf, S. Hawken, S. Ôunpuu, T. Dans, A. Avezum, F. Lanas, M. Mcqueen, A. Budaj, P. Pais, S. Ounpuu, J. Varigos, and L. Lisheng, Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Case-control study, Lancet 364 (2004), pp. 937–952. Available at http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(04)17018-9/fulltext. doi: 10.1016/S0140-6736(04)17018-9
   PubMed Web of Science ®Google Scholar