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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 240435, 7 pages
http://dx.doi.org/10.1155/2014/240435
Research Article

A Semiparametric Bivariate Probit Model for Joint Modeling of Outcomes in STEMI Patients

1Department of Mathematics, Università Degli Studi di Milano, Via Saldini 50, 20133 Milano, Italy
2Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK
3Modeling and Scientific Computing (MOX), Department of Mathematics, Politecnico di Milano, Via Bonardi 9, 20133 Milano, Italy
4Department of Economics, Mathematics and Statistics, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK

Received 8 January 2014; Revised 25 February 2014; Accepted 10 March 2014; Published 1 April 2014

Academic Editor: Guang Wu

Copyright © 2014 Francesca Ieva et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Heckman, “Dummy endogenous variables in a simultaneous equation system,” Econometrica, vol. 46, pp. 931–959, 1978. View at Google Scholar
  2. P. C. Austin, “An introduction to propensity score methods for reducing the effects of confounding in observational studies,” Multivariate Behavioral Research, vol. 46, no. 3, pp. 399–424, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. J. M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, UK, 2010.
  4. D. P. Goldman, J. Bhattacharya, D. F. McCaffrey et al., “Effect of insurance on mortality in an hiv-positive population in care,” Journal of the American Statistical Association, vol. 96, pp. 883–894, 2001. View at Google Scholar
  5. K. M. Johnston, P. Gustafson, A. R. Levy, and P. Grootendorst, “Use of instrumental variables in the analysis of generalized linear models in the presence of unmeasured confounding with applications to epidemiological research,” Statistics in Medicine, vol. 27, no. 9, pp. 1539–1556, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. W. H. Greene, Econometric Analysis, Prentice Hall, New York, NY, USA, 2012.
  7. S. Vansteelandt and E. Goetghebeur, “Causal inference with generalized structural mean models,” Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 65, no. 4, pp. 817–835, 2003. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Marra and R. Radice, “Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity,” Canadian Journal of Statistics, vol. 39, no. 2, pp. 259–279, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. Lombardia, Determinazioni in Merito Alla Rete Per Il Trattamento Dei Pazienti Con Infarto Miocardico Con Tratto St Elevato (Stemi), 2009.
  10. F. Ieva, “Designing and mining a multicenter observational clinical registry concerning patients with acute coronary syndromes,” in New Diagnostic, Therapeutic and Organizational Strategies for Patients with Acute Coronary Syndromes, N. Grieco, M. Marzegalli, and A. M. Paganoni, Eds., pp. 47–60, Springer, 2013. View at Google Scholar
  11. F. Saia, G. Piovaccari, A. Manari et al., “Patient selection to enhance the long-term benefit of first generation drug-eluting stents for coronary revascularisation procedures. Insights from a large multicentre registry,” EuroIntervention, vol. 5, no. 1, pp. 57–66, 2009. View at Google Scholar · View at Scopus
  12. D. Hasdai, S. Behar, L. Wallentin et al., “A prospective survey of the characteristics, treatments and outcomes of patients with acute coronary syndromes in Europe and the Mediterranean basin: The Euro Heart Survey of Acute Coronary Syndromes (Euro Heart Survey ACS),” European Heart Journal, vol. 23, no. 15, pp. 1190–1201, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. G. Campo, P. Guastaroba, A. Marzocchi et al., “Impact of copd on long-term outcome after st-segment elevation myocardial infarction receivingprimary percutaneous coronary intervention,” CHEST Journal, vol. 144, no. 3, pp. 750–757, 2013. View at Google Scholar
  14. T. Schwalm, J. Carlsson, A. Meissner, B. Lagerqvist, and S. James, “Current treatment and outcome of coronary in-stent restenosis in sweden: a report from the swedish coronary angiography and angioplasty registry (scaar),” EuroIntervention, vol. 9, no. 5, pp. 564–572, 2013. View at Google Scholar
  15. T. Gudnason, G. S. Gudnadottir, B. Lagerqvist et al., “Comparison of interventional cardiology in two eu- ropean countries: a nationwide internet based registry study,” International Journal of Cardiology, vol. 168, no. 2, pp. 1237–1242, 2013. View at Google Scholar
  16. M. Dalby, A. Bouzamondo, P. Lechat, and G. Montalescot, “Transfer for primary angioplasty versus immediate thrombolysis in acute myocardial infarction: a meta-analysis,” Circulation, vol. 108, no. 15, pp. 1809–1814, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. A. E. Lindsay, Ecg Learning Centre, 2006.
  18. G. Marra and R. Radice, “SemiParBIVProbit: semiparametric bivariate probit modelling,” R package version 3. 2-9, 2013.
  19. G. S. Maddala, Limited Dependent and Qualitative Variables in Econometrics, Cambridge University Press, Cambridge, UK, 1983.
  20. S. Han and E. J. Vytlacil, “Identification in a generalization of bivariate probit models with endoge- nous regressors,” Working Paper, 2013. View at Google Scholar
  21. J. Wilde, “Identification of multiple equation probit models with endogenous dummy regressors,” Economics Letters, vol. 69, no. 3, pp. 309–312, 2000. View at Google Scholar · View at Scopus
  22. S. Chib and E. Greenberg, “Semiparametric modeling and estimation of instrumental variable models,” Journal of Computational and Graphical Statistics, vol. 16, no. 1, pp. 86–114, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. S. N. Wood, Generalized Additive Models: An Introduction with R, Chapman & Hall/CRC, 2006.
  24. G. Marra and R. Radice, “Penalised regression splines: theory and application to medical research,” Statistical Methods in Medical Research, vol. 19, no. 2, pp. 107–125, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. P. Craven and G. Wahba, “Smoothing noisy data with spline functions—estimating the correct degree of smoothing by the method of generalized cross-validation,” Numerische Mathematik, vol. 31, no. 4, pp. 377–403, 1978. View at Publisher · View at Google Scholar · View at Scopus
  26. C. Gu, Smoothing Spline ANOVA Models, Springer, London, UK, 2002.
  27. G. Marra and S. N. Wood, “Coverage properties of confidence intervals for generalized additive model components,” Scandinavian Journal of Statistics, vol. 39, no. 1, pp. 53–74, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. C. Monfardini and R. Radice, “Practitioners' corner: testing exogeneity in the bivariate probit model: A Monte Carlo Study,” Oxford Bulletin of Economics and Statistics, vol. 70, no. 2, pp. 271–282, 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. K. Pearson, “Mathematical contributions to the theory of evolution. VII. on the correlation of characters not quantitatively measurable,” Philosophical Transactions. Royal Society of London. Series A. Mathematical and Physical Sciences, vol. 195, pp. 1–47, 1900. View at Google Scholar
  30. R. C. Chiburis, J. Das, and M. Lokshin, “A practical comparison of the bivariate probit and linear iv estimators,” World Bank Policy Research Working Paper 5601, 2011. View at Google Scholar
  31. F. Ieva and A. M. Paganoni, “Process indicators for assessing quality of hospital care: a case study on stemi patients,” JP Journal of Biostatistics, vol. 6, pp. 53–75, 2011. View at Google Scholar
  32. N. Grieco, F. Ieva, and A. M. Paganoni, “Performance assessment using mixed effects models: a case study on coronary patient care,” IMA Journal Management Mathematics, vol. 23, no. 2, pp. 117–131, 2012. View at Publisher · View at Google Scholar · View at Scopus
  33. A. Guglielmi, F. Ieva, A. M. Paganoni, and F. Ruggeri, “A bayesian random effects model for survival probabilities after acute myocardial infarction,” Chilean Journal of Statistics, vol. 3, pp. 1–15, 2012. View at Google Scholar
  34. A. Guglielmi, F. Ieva, A. M. Paganoni, F. Ruggeri, and J. Soriano, “Semiparametric bayesian modelingfor the classification of patients with high observed survival probabilities,” Journal of the Royal Statistical Society—Series C, Forthcoming, 2013.
  35. B. J. Gersh, G. W. Stone, H. D. White, and D. R. Holmes Jr., “Pharmacological facilitation of primary percutaneous coronary intervention for acute myocardial infarction: is the slope of the curve the shape of the future?” Journal of the American Medical Association, vol. 293, no. 8, pp. 979–986, 2005. View at Publisher · View at Google Scholar · View at Scopus
  36. F. Ieva and A. M. Paganoni, “Multilevel models for clinical registers concerning stemi patients in a complex urban reality: a statistical analysis of momi2 survey,” Communications in Applied and Industrial Mathematics, vol. 1, pp. 128–147, 2010. View at Google Scholar