Modern modelling techniques are data hungry: A simulation study for predicting dichotomous endpoints

BMC Medical Research Methodology

Volumn 14, Issue 1, 2014, Pages

  Van Der Ploeg, Tjeerd ^a,c   Austin, Peter C ^b   Steyerberg, Ewout W ^c  

^a Location Alkmaar   (Netherlands)

^b UNIVERSITY OF TORONTO   (Canada)

^c ERASMUS MEDICAL CENTER   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BIOASSAY; BRAIN INJURIES; FACTUAL DATABASE; HEAD AND NECK NEOPLASMS; HEAD INJURIES, CLOSED; HUMAN; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL ANALYSIS; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; TREATMENT OUTCOME;

BRAIN INJURIES; DATA INTERPRETATION, STATISTICAL; DATABASES, FACTUAL; ENDPOINT DETERMINATION; HEAD AND NECK NEOPLASMS; HEAD INJURIES, CLOSED; HUMANS; MODELS, STATISTICAL; ROC CURVE; SUPPORT VECTOR MACHINE; TREATMENT OUTCOME;

EID: 84923873960     PISSN: None     EISSN: 14712288     Source Type: Journal    
DOI: 10.1186/1471-2288-14-137     Document Type: Article

References (26)

1. Steyerberg EW, Moons KGM, van der Windt DA, Hayden JA, Perel P, Schroter S, Riley RD, Hemingway H, Altman DG: Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013, 10.
2. Harrison RF, Kennedy RL: Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation. Ann Emerg Med 2005, 46:431-439.
3. Mangasarian Y-JLOL, Wolberg WH: Breast cancer survival and chemotherapy: a support vector machine analysis. In Discret Math Probl with Med Appl DIMACS Work Discret Math Probl with Med Appl December 8-10, 1999, Volume 55. DIMACS Cent; 2000:1.
4. Ennis M, Hinton G, Naylor D, Revow M, Tibshirani R: A comparison of statistical learning methods on the Gusto database. Stat Med 1998, 17:2501-2508.
5. Van der Ploeg T, Datema F, de Jong RB, Steyerberg EW: Prediction of Survival with Alternative Modeling Techniques Using Pseudo Values. PLoS One 2014, 9:e100234.
6. Van der Ploeg T, Smits M, Dippel DW, Hunink M, Steyerberg EW: Prediction of intracranial findings on CT-scans by alternative modelling techniques. BMC Med Res Methodol 2011, 143.
7. Selker HP, Griffith JL, Patil S, Long WJ, D'Agostino RB: A Comparison of Performance of Mathematical Predictive Methods for Medical Diagnosis: Identifying Acute Cardiac Ischemia among Emergency Department Patients. 1995, 43:468-476.
8. John G, Langley P, John H: Static Versus Dynamic Sampling for Data Mining. KDD 1996, :367-370.
9. Provost F, Jensen D, Oates T: Efficient Progressive Sampling. Proc fifth ACM SIGKDD Int Conf Knowl Discov data Min 1999, :23-32.
10. Morgan J, Daugherty R, Hilchie A, Carey B: Sample size and modeling accuracy of decision tree based data mining tools. Acad Inf Manag Sci J 2003, 6:71-99.
11. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR: A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996, 49:1373-1379.
12. Harrell FE: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer; 2001.
13. Steyerberg EW, Harrell FE, Borsboom GJJM, Eijkemans MJC, Vergouwe Y, Habbema JDF: Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001, 54:774-781.
14. Steyerberg EW, Eijkemans MJC, Harrell FE, Habbema JDF: Prognostic modelling with logistic regression analysis: A comparison of selection and estimation methods in small data sets. Stat Med 2000, 19:1059-1079.
15. Steyerberg EW: Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. 2009, 19:500.
16. Tzoulaki I, Liberopoulos G, Ioannidis JPA: Assessment of claims of improved prediction beyond the Framingham risk score. JAMA 2009, 302:2345-2352.
17. Lee DB: Requiem for large-scale models. ACM SIGSIM Simul Dig 1975, :16-29.
18. Datema FR, Ferrier MB, van der Schroeff MP, de Jong RJ B: Impact of comorbidity on short-term mortality and overall survival of head and neck cancer patients. Head Neck 2010, 32:728-736.
19. Marmarou A, Lu J, Butcher I, McHugh GS, Mushkudiani NA, Murray GD, Steyerberg EW, Maas AIR: IMPACT database of traumatic brain injury: design and description. J Neurotrauma 2007, 24:239-250.
20. Tufféry S: Data Mining and Statistics for Decision Making. John Wiley & Sons; 2011.
21. Breiman L, Friedman JH, Olshen RA, Stone CJ: Classification and Regression Trees. 1984, 19:368.
22. R Development Core Team R: R: A Language and Environment for Statistical Computing. R Found Stat Comput 2011, 409.
23. Kurahashi I: KsPlot: Check the power of seveal statistical models. 2011.
24. Austin PC, Lee DS, Steyerberg EW, Tu JV: Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods? Biometrical J 2012, 54:657-673.
25. Young NH, Andrews PJD: Developing a Prognostic Model for Traumatic Brain Injury - A Missed Opportunity? PLoS Med 2008, 5:3.
26. Harrell FE, Lee KL, Mark DB: Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996, 15: 361-387.