Erratum: How to develop a more accurate risk prediction model when there are few events (BMJ (2015) 351 (h3868)) DOI: 10.1136/bmj.h3868);How to develop a more accurate risk prediction model when there are few events

BMJ (Online)

Volumn 351, Issue , 2015, Pages

  Pavlou, Menelaos ^a   Ambler, Gareth ^a   Seaman, Shaun R ^b   Guttmann, Oliver ^a   Elliott, Perry ^c   King, Michael ^a   Omar, Rumana Z ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b MRC BIOSTATISTICS UNIT   (United Kingdom)

^c UNIVERSITY COLLEGE LONDON HOSPITALS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; BOOTSTRAPPING; CALCULATION; HUMAN; INTERMETHOD COMPARISON; MATHEMATICAL MODEL; PENALISED REGRESSION; PREDICTION; PRIORITY JOURNAL; PROBABILITY; REGRESSION ANALYSIS; RISK ASSESSMENT; SAMPLE SIZE; PROCEDURES; REPRODUCIBILITY; STATISTICAL MODEL;

MODELS, STATISTICAL; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; SAMPLE SIZE;

EID: 84942465542     PISSN: 09598146     EISSN: 17561833     Source Type: Journal    
DOI: 10.1136/bmj.i3235     Document Type: Erratum

References (22)

1. Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how? BMJ 2009;338:b375.
2. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models a framework for traditional and novel measures. Epidemiology 2010;21:128-38.
3. Ambler G, Omar R, Royston P, et al. Generic, simple risk stratification model for heart valve surgery. Circulation 2005;112:224-31.
4. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 2007;335:136.
5. D'Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation 2008;117:743-53.
6. O'Mahony C, Jichi F, Pavlou M, et al. A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy. Eur Heart J 2013;35:2010-20.
7. Harrell FE Jr. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, 2001.
8. Omar R, Morton L, Halliday D, et al. Outlet strut fracture of Bjork-Shiley convexo concave heart valves: the UK cohort study. Heart 2001;86:57-62.
9. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 2015;350:g7594.
10. Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 1995;48:1503-10.
11. Steyerberg EW, Eijkemans MJ, Harrell FE Jr, Habbema JD. Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets. Med Decision Making 2001;21:45-56.
12. Ye J. On measuring and correcting the effects of data mining and model selection. J Am Stat Assoc 1998;93:120-31.
13. Zou H, Hastie T. Regularization and variable selection via the elastic net. J Roy Statist Soc Ser B 2005;67:301-20.
14. Ambler G, Seaman S, Omar RZ. An evaluation of penalised survival methods for developing prognostic models with rare events. Stat Med 2011;31:1150-61.
15. Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ 2009;338:b606.
16. Robert Tibshirani. Regression shrinkage and selection via the lasso. J Roy Statist Soc Ser B 1994;58:267-88.
17. Verweij PJM, Van Houwelingen HC. Penalized likelihood in Cox regression. Stat Med 1994;13:2427-36.
18. Cessie SL, Houwelingen JCV. Ridge estimators in logistic regression. J Roy Statist Soc Ser C 1992;41:191-201.
19. Hosmer DW Jr, Lemeshow S. Applied logistic regression. John Wiley & Sons, 2004.
20. Gelman A, Jakulin A, Pittau MG, Su Y.-S. A weakly informative default prior distribution for logistic and other regression models. Ann Appl Stat 2008:13 60-83.
21. Debray TPA, Koffijberg H, Nieboer D, et al. Meta-analysis and aggregation of multiple published prediction models. Stat Med 2014;33:2341-62.
22. Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med 2002;21:2409-19.