Five myths about variable selection

Transplant International

Volumn 30, Issue 1, 2017, Pages 6-10

  Heinze, Georg ^a   Dunkler, Daniela ^a  

^a MEDICAL UNIVERSITY OF VIENNA   (Austria)

Author keywords

association; explanatory models; multivariable modeling; prediction; statistical analysis

Indexed keywords

CONFIDENCE INTERVAL; HUMAN; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; REVIEW; SAMPLE SIZE; SENSITIVITY ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL; UNCERTAINTY; VARIABLE SELECTION; COMPUTER; METHODOLOGY; MULTIVARIATE ANALYSIS; PROCEDURES; REGRESSION ANALYSIS; SOFTWARE; TRANSPLANTATION;

COMPUTERS; DATA INTERPRETATION, STATISTICAL; HUMANS; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; REGRESSION ANALYSIS; RESEARCH DESIGN; SAMPLE SIZE; SOFTWARE; TRANSPLANTATION;

EID: 85007342670     PISSN: 09340874     EISSN: 14322277     Source Type: Journal    
DOI: 10.1111/tri.12895     Document Type: Review

References (29)

1. Tripepi G, Jager KJ, Dekker FW, Zoccali C. Testing for causality and prognosis: etiological and prognostic models. Kidney Int 2008; 74: 1512.
2. Von Düring ME, Jenssen T, Bollerslev J, et al. Visceral fat is better related to impaired glucose metabolism than body mass index after kidney transplantation. Transpl Int 2015; 28: 1162.
3. Bhat M, Hathcock M, Kremers WK, et al. Portal vein encasement predicts neoadjuvant therapy response in liver transplantation for perihilar cholangiocarcinoma protocol. Transpl Int 2015; 28: 1383.
4. Pianta TJ, Peake PW, Pickering JW, Kelleher M, Buckley NA, Endre ZH. Evaluation of biomarkers of cell cycle arrest and inflammation in prediction of dialysis or recovery after kidney transplantation. Transpl Int 2015; 28: 1392.
5. Rompianesi G, Montalti R, Cautero N, et al. Neurological complications after liver transplantation as a consequence of immunosuppression: univariate and multivariate analysis of risk factors. Transpl Int 2015; 28: 864.
6. Zijlstra LE, Constantinescu AA, Manintveld O, et al. Improved long-term survival in Dutch heart transplant patients despite increasing donor age: the Rotterdam experience. Transpl Int 2015; 28: 962.
7. Fernández-Ruiz M, Arias M, Campistol JM, et al. Cytomegalovirus prevention strategies in seropositive kidney transplant recipients: an insight into current clinical practice. Transpl Int 2015; 28: 1042.
8. Martinez-Selles M, Almenar L, Paniagua-Martin MJ, et al. Donor/recipient sex mismatch and survival after heart transplantation: only an issue in male recipients? An analysis of the Spanish Heart Transplantation Registry. Transpl Int 2015; 28: 305.
9. Rodríguez-Perálvarez M, García-Caparrõs C, Tsochatzis E, et al. Lack of agreement for defining ‘clinical suspicion of rejection’ in liver transplantation: a model to select candidates for liver biopsy. Transpl Int 2015; 28: 455.
10. Pezawas T, Grimm M, Ristl R, et al. Primary preventive cardioverter-defibrillator implantation (Pro-ICD) in patients awaiting heart transplantation. A prospective, randomized, controlled 12-year follow-up study. Transpl Int 2015; 28: 34.
11. Prasad GVR, Huang M, Silver SA, et al. Metabolic syndrome definitions and components in predicting major adverse cardiovascular events after kidney transplantation. Transpl Int 2015; 28: 79.
12. Tripon S, Francoz C, Albuquerque A, et al. Interactions between virus-related factors and post-transplant ascites in patients with hepatitis C and no cirrhosis: role of cryoglobulinemia. Transpl Int 2015; 28: 162.
13. Somers J, Ruttens D, Verleden SE, et al. A decade of extended-criteria lung donors in a single center: was it justified? Transpl Int 2015; 28: 170.
14. Nagai S, Mangus RS, Anderson E, et al. Post-transplant persistent lymphopenia is a strong predictor of late survival in isolated intestine and multivisceral transplantation. Transpl Int 2015; 28: 1195–204.
15. Harrell FEJ. Regression Modeling Strategies, 2nd edn. Switzerland: Springer, 2015.
16. IBM Corp. IBM Statistics for Windows, 23.0 edn. New York, NY: IBM Corp, 2013.
17. SAS Institute Inc. SAS/STAT, 9.4 edn. Cary, NC: SAS Institute Inc., 2012.
18. Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol 2007; 165: 710.
19. Steyerberg EW. Clinical Prediction Models. New York, NY: Springer, 2009.
20. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999; 10: 37.
21. VanderWeele TJ, Shpitser I. A new criterion for confounder selection. Biometrics 2011; 67: 1406.
22. Sung G-W, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 1996; 49: 907.
23. Dunkler D, Plischke M, Leffondré K, Heinze G. Augmented backward elimination: a pragmatic and purposeful way to develop statistical models. PLoS One 2014; 9: e113677. doi:10.1371/journal.pone.0113677
24. Goodman S. A dirty dozen: twelve P-value misconceptions. Semin Hematol 2008; 45: 135.
25. Burnham KP, Anderson DR. Model Selection and Multimodel Inference, 2nd edn. New York, NY: Springer, 2002.
26. Sauerbrei W, Schumacher M. A bootstrap resampling procedure for model building: application to the Cox regression model. Stat Med 1992; 11: 2093.
27. Royston P, Sauerbrei W. Multivariable Model-Building – A Pragmatic Approach to Regression Analysis based on Fractional Polynomials for Modelling Continuous Variables. Chichester: John Wiley & Sons Ltd, 2008.
28. Tibshirani R. Regression shrinkage and selection via the Lasso. J Roy Stat Soc B Met 1996; 58: 267.
29. Breiman L. Arcing classifiers. Ann Stat 1998; 26: 801.