External Validation of Models for Predicting Disability in Community-Dwelling Older People in the Netherlands: A Comparative Study

References

• Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media; 2009.
   Google Scholar
• Harrell FE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer; 2001.
   Google Scholar
• Steyerberg EW. Clinical Prediction Models. Springer; 2019.
   Google Scholar
• Young NH, Andrews PJD. Developing a prognostic model for traumatic brain injury. A missed opportunity? PLoS Med. 2008;5(8):e168. doi:10.1371/journal.pmed.0050168
   PubMed Web of Science ®Google Scholar
• Sewak M, Vaidya P, Chan CC, Duan ZH. SVM approach to breast cancer classification. In: Second international multisymposiums on computer and computational sciences (IMSCCS 2007). IEEE; 2007:32–37.
   Google Scholar
• Biganzoli E, Boracchi P, Mariani L, Marubini E. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat Med. 1998;17(10):1169–1186. doi:10.1002/(SICI)1097-0258(19980530)17:10<1169::AID-SIM796>3.0.CO;2-D
   PubMed Web of Science ®Google Scholar
• Van der Ploeg T, Gobbens RJ, Salem BE. Bayesian techniques in predicting frailty among community-dwelling older adults in the Netherlands. Arch Gerontol Geriatr. 2022;105:104836. doi:10.1016/j.archger.2022.104836
   PubMed Web of Science ®Google Scholar
• Xu W, Zhang J, Zhang Q, Wei X Risk prediction of type II diabetes based on random forest model. In: 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). IEEE; 2017:382–386.
   Google Scholar
• Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonca A. Data mining methods in the prediction of Dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes. 2011;4(1):1–14. doi:10.1186/1756-0500-4-299
   PubMedGoogle Scholar
• Terrin N, Schmid CH, Griffith JL, D’Agostino RB Sr, Selker HP. External validity of predictive models: a comparison of logistic regression, classification trees, and neural networks. J Clin Epidemiol. 2003;56(8):721–729. doi:10.1016/S0895-4356(03)00120-3
   PubMed Web of Science ®Google Scholar
• Ecke TH, Hallmann S, Koch S, et al. External validation of an artificial neural network and two nomograms for prostate cancer detection. Internat Schol Res Notices. 2012;2012:1.
   Google Scholar
• Van der Ploeg T, Nieboer D, Steyerberg EW. Modern modeling techniques had limited external validity in predicting mortality from traumatic brain injury. J Clin Epidemiol. 2016;78:83–89. doi:10.1016/j.jclinepi.2016.03.002
   PubMed Web of Science ®Google Scholar
• Konig IR, Malley J, Weimar C, Diener HC, Ziegler A. Practical experiences on the necessity of external validation. Statist Med. 2007;26(30):5499–5511. doi:10.1002/sim.3069
   PubMed Web of Science ®Google Scholar
• Debray TP, Vergouwe Y, Koffijberg H, Nieboer D, Steyerberg EW, Moons KG. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol. 2015;68(3):279–289. doi:10.1016/j.jclinepi.2014.06.018
   PubMed Web of Science ®Google Scholar
• Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology. 2010;21(1):128. doi:10.1097/EDE.0b013e3181c30fb2
   PubMed Web of Science ®Google Scholar
• Austin PC, Steyerberg EW. Predictive accuracy of risk factors and markers: a simulation study of the effect of novel markers on different performance measures for logistic regression models. Stat Med. 2013;32(4):661–672. doi:10.1002/sim.5598
   PubMed Web of Science ®Google Scholar
• Vergouwe Y, Moons KG, Steyerberg EW. External validity of risk models: use of benchmark values to disentangle a case-mix effect from incorrect coefficients. Am J Epidemiol. 2010;172(8):971–980. doi:10.1093/aje/kwq223
   PubMed Web of Science ®Google Scholar
• Kempen GIJM, Miedema I, Ormel J, Molenaar W. Groningen activity restriction scale. Soc Sci Med. 1993;1993:1.
   Google Scholar
• Gobbens RJ, Van assen M. The prediction of quality of life by physical, psychological and social components of frailty in community-dwelling older people. Quality of Life Res. 2014;23(8):2289–2300. doi:10.1007/s11136-014-0672-1
   PubMed Web of Science ®Google Scholar
• AARP. Aging readiness and competitiveness; 2018. Available from: https://www.aarpinternational.org/initiatives/aging-readiness-competitiveness-arc/netherlands. Accessed November 18, 2022.
   Google Scholar
• Bally EL, Ye L, Van Grieken A, et al. Factors associated with falls among hospitalized and community-dwelling older adults: the APPCARE study. Front Public Health. 2023;2023:11.
   Google Scholar
• Breiman L, Friedman J, Olshen R, Stone C. Classification and Regression Trees. Whole Wadsworth; 1984:368.
   Google Scholar
• Harrell FE Jr. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer; 2015.
   Google Scholar
• Tufféry S. Data Mining and Statistics for Decision Making. John Wiley & Sons; 2011.
   Google Scholar
• Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297. doi:10.1007/BF00994018
   Web of Science ®Google Scholar
• Tsamardinos I, Aliferis CF. Towards Principled Feature Selection: Relevancy, Filters and Wrappers. AISTATS; 2003.
   Google Scholar
• Aliferis CF, Tsamardinos I, Statnikov AR, Brown LE. Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery. Vol. 3. METMBS; 2003:371–376.
   Google Scholar
• Van der Ploeg T, Steyerberg EW. Feature selection and validated predictive performance in the domain of Legionella pneumophila: a comparative study. BMC Res Notes. 2016;9(1):1–7. doi:10.1186/s13104-016-1945-2
   PubMedGoogle Scholar
• Van der Ploeg T, Gobbens R, et al. A comparison of different modeling techniques in predicting mortality with the Tilburg frailty indicator: longitudinal study. JMIR Med Inform. 2022;10(3):e31480. doi:10.2196/31480
   PubMedGoogle Scholar
• Gareth J, Daniela W, Trevor H, Robert T. An Introduction to Statistical Learning: With Applications in R. Spinger; 2013.
   Google Scholar
• Boser BE, Guyon IM, Vapnik VN A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory; 1992:144–152.
   Google Scholar
• Ripley BD. Neural networks and related methods for classification. J Royal Stat Soc. 1994;56(3):409–437.
   Google Scholar
• Cutler A, Cutler DR, Stevens JR. Random forests. Ensemble Mach Learn. 2012;2012:157–175.
   Google Scholar
• Van Calster B, McLernon DJ, Van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17(1):1–7. doi:10.1186/s12916-019-1466-7
   PubMed Web of Science ®Google Scholar
• Team RC. R: A Language and Environment for Statistical Computing [Software]. Vienna: R Foundation for Statistical Computing; 2019.
   Google Scholar
• Bleeker SE, Moll HA, Steyerberg EW, et al. External validation is necessary in prediction research: a clinical example. J Clin Epidemiol. 2003;56(9):826–832. doi:10.1016/S0895-4356(03)00207-5
   PubMed Web of Science ®Google Scholar
• Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med. 1999;130(6):515–524. doi:10.7326/0003-4819-130-6-199903160-00016
   PubMed Web of Science ®Google Scholar
• Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M. External validation of prognostic models: what, why, how, when and where? Clin Kidney J. 2021;14(1):49–58. doi:10.1093/ckj/sfaa188
   PubMed Web of Science ®Google Scholar
• Siontis GC, Tzoulaki I, Castaldi PJ, Ioannidis JP. External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. J Clin Epidemiol. 2015;68(1):25–34. doi:10.1016/j.jclinepi.2014.09.007
   PubMed Web of Science ®Google Scholar
• Gobbens RJ, Van der Ploeg T. Frailty at Risk Scale (FARS): development and application. Eur J Ageing. 2022;19(2):301–308. doi:10.1007/s10433-021-00628-4
   PubMed Web of Science ®Google Scholar
• Gobbens RJ, Van Assen MA, Luijkx KG, Wijnen-Sponselee MT, Schols JM. The Tilburg frailty indicator: psychometric properties. J Am Med Dir Assoc. 2010;11(5):344–355. doi:10.1016/j.jamda.2009.11.003
   PubMed Web of Science ®Google Scholar
• Suurmeijer T, Doeglas DM, Moum T, et al. The Groningen Activity Restriction Scale for measuring disability: its utility in international comparisons. Am J Public Health. 1994;84(8):1270–1273. doi:10.2105/AJPH.84.8.1270
   PubMed Web of Science ®Google Scholar
• Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol a Biol Sci Med Sci. 2001;56(3):M146–M157. doi:10.1093/gerona/56.3.M146
   PubMed Web of Science ®Google Scholar
• CCMO. Research involving human subjects; 2016. Available from: https://english.ccmo.nl/investigators/legal-framework-for-medical-scientific-research/your-research-is-it-subject-to-the-wmo-or-not. Accessed June 20, 2016.
   Google Scholar