Dynamic models to predict health outcomes: current status and methodological challenges

David Jenkins1, Matthew Sperrin1, Glen P. Martin1, Niels Peek2
1Health e-Research Centre, Farr Institute, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
2NIHR Greater Manchester Patient Safety Translational Research Centre, The University of Manchester, Manchester, UK

Tóm tắt

Từ khóa


Tài liệu tham khảo

Five Year Forward View. (2014).

Salive ME. Multimorbidity in older adults. Epidemiol Rev. 2013;35:75–83.

Divo MJ, Martinez CH, Mannino DM. Ageing and the epidemiology of multimorbidity. Eur Respir J. 2014;44:1055–68.

Watkins J, et al. Effects of health and social care spending constraints on mortality in England: a time trend analysis. BMJ Open. 2017;7:e017722.

Abu-Hanna A, Lucas PJF. Prognostic models in medicine AI and Statistical Approaches. Method Inf Med. 2001;40:1–5.

Steyerberg E, et al. Prognosis research strategy (PROGRESS) series 3: prognostic model research. PLoS Med. 2013;10:e1001381.

Damen JAAG, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. Bmj. 2016:i2416. https://doi.org/10.1136/bmj.i2416 .

Hippisley-Cox J, et al. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. Br Med J. 2007;335:136–41.

Davis SE, Lasko TA, Chen G, Siew ED, Matheny ME. Calibration drift in regression and machine learning models for acute kidney injury. J Am Med Informatics Assoc. 2017;24:1052–61.

Hickey GL, et al. Dynamic trends in cardiac surgery: why the logistic EuroSCORE is no longer suitable for contemporary cardiac surgery and implications for future risk models. Eur J Cardiothoracic Surg. 2013;43:1146–52.

Hippisley-Cox J, Coupland C, Robson J, Brindle P. Derivation validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. Bmj. 2011;342:93.

Siregar S, et al. Improved prediction by dynamic modeling. Circ Cardiovasc Qual Outcomes. 2016;9:171–81.

Su T-L, Jaki T, Hickey GL, Buchan I, Sperrin MA. Review of statistical updating methods for clinical prediction models. Stat Methods Med Res. 2016:1–16. https://doi.org/10.1177/0962280215626466 .

Janssen KJM, Moons KGM, Kalkman CJ, Grobbee DE, Vergouwe Y. Updating methods improved the performance of a clinical prediction model in new patients. J Clin Epidemiol. 2008;61:76–86.

Steyerberg EW, Borsboom GJJM, van Houwelingen HC, Eijkemans MJC, Habbema JDF. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med. 2004;23:2567–86.

van Houwelingen HC, Thorogood J. Construction, validation and updating of a prognostic model for kidney graft survival. Stat Med. 1995;14:1999–2008.

Hickey GL, et al. Dynamic prediction modeling approaches for cardiac surgery. Circ. Cardiovasc Qual. Outcomes. 2013;6:649–58.

Draper NR, Van Nostrand RC. Ridge regression and James-Stein estimation: review and comments. Technometrics. 1979;21(4):451–66.

Copas J. Regression prediction and shrinkage. R Stat Soc. 1983;45:311–54.

Raftery AE, Ettler P. Online prediction under model uncertainty via dynamic model averaging : application to a cold rolling mill. Technometrics. 2010;52:52–66.

Mccormick TH, Raftery AE, Madigan D, Burd RS. Dynamic logistic regression and dynamic model averaging for binary classification. Biometrics. 2012;68:23–30.

Finkelman BS, French B, Kimmel SE. The prediction accuracy of dynamic mixed-effects models in clustered data. BioData Min. 2016;9:5.

Toll DB, Janssen KJM, Vergouwe Y, Moons KGM. Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol. 2008;61:1085–94.

Fan J, Zhang W. Statistical methods with varying coefficient models. Stat Interface. 2008;1:179–95.

Hoover DR, Rice JA, Wu CO, Yang L-P. Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data. Biometrika. 1998;85:809–22.

Madigan D, Raftery AE. Model selection and accounting for model uncertainty in graphical models using Occam’s window; 1991.

Onorante L, Raftery AE. Dynamic model averaging in large model spaces using dynamic Occam’s window. Eur Econ Rev. 2016;81:2–14.

Nashef SAM, et al. European system for cardiac operative risk evaluation (EuroSCORE). Eur J Cardiothoracic Surg. 1999;16:9–13.

Ohata T, Kaneko M, Kuratani T, Ueda H, Shimamura K. Using the EuroSCORE to assess changes in the risk profiles of the patients undergoing coronary artery bypass grafting before and after the introduction of less invasive coronary surgery. Ann Thorac Surg. 2005;80:131–5.

McCormick TH, Raftery A, Madigan D. dma: dynamic model averaging; 2018.

Ramsay JO, Wickham H, Graves S, Hooker G. fda: functional data analysis; 2017.

Altman DG, Royston P. What do we mean by validating a prognistic model? Stat Med. 2000;19:453–73.

Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med. 1999;130:515–24.

Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute physiology and chronic health evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med. 2006;34:1297–310.

Vergouwe Y, et al. A closed testing procedure to select an appropriate method for updating prediction models. Stat Med. 2017;36:4529–39.

Hafkamp-De Groen E, et al. Predicting asthma in preschool children with asthma-like symptoms: validating and updating the PIAMA risk score. J Allergy Clin Immunol. 2013;132:1303–10.

Genders TSS, et al. A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating and extension. Eur Heart J. 2011;32:1316–30.

Moons KGM, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart. 2012;98:691–8.