Journal of Clinical Epidemiology
Volume 63, Issue 10 , Pages 1145-1155 , October 2010

Logistic regression had superior performance compared with regression trees for predicting in-hospital mortality in patients hospitalized with heart failure

  • Peter C. Austin

      Affiliations

    • Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
    • Department of Health Management, Policy and Evaluation, University of Toronto, Toronto, Ontario, Canada
    • Corresponding Author InformationCorresponding author. Institute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada. Tel.: +416-480-6131; fax: +416-480-6048.
  • ,
  • Jack V. Tu

      Affiliations

    • Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
    • Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
    • Department of Health Management, Policy and Evaluation, University of Toronto, Toronto, Ontario, Canada
    • Schulich Heart Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
    • Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
  • ,
  • Douglas S. Lee

      Affiliations

    • Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
    • Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
    • Division of Cardiology, University Health Network, Toronto, Ontario, Canada

,Accepted 22 December 2009.

References 

  1. Lee DS, Austin PC, Rouleau JL, Liu PP, Naimark D, Tu JV. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model. JAMA. 2003;290:2581–2587
  2. Fonarow GC, Adams KF, Abraham WT, Yancy CW. Risk stratification for in-hospital mortality in acutely decompensated heart failure. JAMA. 2005;293:572–580
  3. Tu JV, Jaglal SB, Naylor CD. Multicenter validation of a risk index for mortality, intensive care unit stay, and overall hospital length of stay after cardiac surgery. Steering Committee of the Provincial Adult Cardiac Care Network of Ontario. Circulation. 1995;91:677–684
  4. Lee KL, Woodlief LH, Topol EJ, Weaver WD, Betriu A, Col J, et al. Predictors of 30-day mortality in the era of reperfusion for acute myocardial infarction. Circulation. 1995;91:1659–1668
  5. Sullivan LM, Massaro JM, D'Agostino RB. Presentation of multivariate data for clinical use: The Framingham Study risk score functions. Stat Med. 2004;23:1631–1660
  6. Breiman L, Freidman JH, Olshen RA, Stone CJ. Classification and regression trees. Boca Raton, FL: Chapman&Hall/CRC; 1998;
  7. Sauerbrei W, Madjar H, Prompeler HJ. Differentiation of benign and malignant breast tumors by logistic regression and a classification tree using Doppler flow signals. Methods Inf Med. 1998;37:226–234
  8. Gansky SA. Dental data mining: potential pitfalls and practical issues. Adv Dent Res. 2003;17:109–114
  9. Nelson LM, Bloch DA, Longstreth WT, Shi H. Recursive partitioning for the identification of disease risk subgroups: a case-control study of subarachnoid hemorrhage. J Clin Epidemiol. 1998;51:199–209
  10. Lemon SC, Roy J, Clark MA, Friedmann PD, Rakowski W. Classification and regression tree analysis in public health: methodological review and comparison with logistic regression. Ann Behav Med. 2003;26:172–181
  11. Austin PC, Tu JV. Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. J Clin Epidemiol. 2004;57:1138–1146
  12. Austin PC. The large-sample performance of backwards variable elimination. J Appl Stat. 2008;35:1355–1370
  13. Derkson S, Keselman HJ. Backward, forward and stepwise automated subset selection algorithms: frequency of obtaining authentic and noise variables. Br J Math Stat Psychol. 1992;45:265–282
  14. Flack VF, Chang PC. Frequency of selecting noise variables in subset regression analysis: a simulation study. Am Stat. 1987;14:84–86
  15. Tu JV, Donovan LR, Lee DS, Austin PC, Ko DT, Wang JT, et al. Quality of cardiac care in Ontario. Ontario, Canada: Institute for Clinical Evaluative Sciences; 2004;
  16. Tu JV, Donovan LR, Lee DS, Wang JT, Austin PC, Alter DA, et al. Effectiveness of public report cards for improving the quality of cardiac care: the EFFECT study: a randomized trial. JAMA. 2009;302:2330–2337
  17. Hastie TJ, Tibshirani RJ. Generalized additive models. London, UK: Chapman & Hall; 1990;
  18. Clark LA, Pregibon D. Tree-based methods. In:  Chambers JM,  Hastie TJ editor. Statistical models in S. New York, NY: Chapman & Hall; 1993;p. 377–419
  19. Harrell FE. Regression modeling strategies. New York, NY: Springer-Verlag; 2001;
  20. Nagelkerke NJD. A note on a general definition of the coefficient of determination. Biometrika. 1991;78:691–692
  21. Cragg JG, Uhler R. The demand for automobiles. Can J Econ. 1970;3:386–406
  22. Austin PC. A comparison of classification and regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality. Stat Med. 2007;26:2937–2957
  23. R Core Development Team . R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2005;
  24. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. Data mining, inference, and prediction. New York, NY: Springer-Verlag; 2001;
  25. Vazquez R, Bayes-Genis A, Cygankiewicz I, Pascual-Figal D, Grigorian-Shamagian L, Pavon R, et al. MUSIC Investigators The MUSIC Risk score: a simple method for predicting mortality in ambulatory patients with chronic heart failure. Eur Heart J. 2009;30:1088–1096
  26. Abraham WT, Fonarow GC, Albert NM, Stough WG, Gheorghiade M, Greenberg BH, et al. OPTIMIZE-HF Investigators and Coordinators Predictors of in-hospital mortality in patientshospitalized for heart failure: insights from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF). J Am Coll Cardiol. 2008;52:347–356
  27. Jones RC, Francis GS, Lauer MS. Predictors of mortality in patients with heart failure and preserved systolic function in the Digitalis Investigation Group trial. J Am Coll Cardiol. 2004;44:1025–1029
  28. Brophy JM, Dagenais GR, McSherry F, Williford W, Yusuf S. A multivariate model for predicting mortality in patients with heart failure and systolic dysfunction. Am J Med. 2004;116:300–304
  29. Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, et al. The Seattle heart failure model: prediction of survival in heart failure. Circulation. 2006;113:1424–1433

PII: S0895-4356(09)00389-8

doi: 10.1016/j.jclinepi.2009.12.004

Journal of Clinical Epidemiology
Volume 63, Issue 10 , Pages 1145-1155 , October 2010