The uncertainty with using risk prediction models for individual decision making: an exemplar cohort study examining the prediction of cardiovascular disease in English primary care
暂无分享,去创建一个
[1] Glen P Martin,et al. Dynamic models to predict health outcomes: current status and methodological challenges , 2018, Diagnostic and Prognostic Research.
[2] T. Nolan,et al. The NHS heart age test will overload GPs who are already under huge pressure , 2018, British Medical Journal.
[3] Nita G Forouhi,et al. Food based dietary patterns and chronic disease prevention , 2018, British Medical Journal.
[4] Karel G M Moons,et al. Treatment use in prognostic model research: a systematic review of cardiovascular prognostic studies , 2017, Diagnostic and Prognostic Research.
[5] J. Hippisley-Cox,et al. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study , 2017, British Medical Journal.
[6] R. Omar,et al. Review and evaluation of performance measures for survival prediction models in external validation settings , 2017, BMC Medical Research Methodology.
[7] P. Austin. A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications , 2017, International statistical review = Revue internationale de statistique.
[8] Ramachandran S Vasan,et al. Biomarkers in cardiovascular disease: Statistical assessment and section on key novel heart failure biomarkers. , 2017, Trends in cardiovascular medicine.
[9] Iain Buchan,et al. Clinical prediction in defined populations: a simulation study investigating when and how to aggregate existing models , 2017, BMC Medical Research Methodology.
[10] P. Wilkinson,et al. Primary prevention of cardiovascular disease: A review of contemporary guidance and literature , 2017, JRSM cardiovascular disease.
[11] A. Berezin. Biomarkers for cardiovascular risk in patients with diabetes , 2016, Heart.
[12] N. Townsend,et al. Trends in the epidemiology of cardiovascular disease in the UK , 2016, Heart.
[13] G. Collins,et al. Prediction models for cardiovascular disease risk in the general population: systematic review , 2016, British Medical Journal.
[14] C. Allgulander. Anxiety as a risk factor in cardiovascular disease , 2016, Current opinion in psychiatry.
[15] C. Goodman,et al. ReseArch with Patient and Public invOlvement: a RealisT evaluation – the RAPPORT study , 2015 .
[16] J. Kai,et al. The value of aspartate aminotransferase and alanine aminotransferase in cardiovascular disease risk assessment , 2015, Open Heart.
[17] K. Bhaskaran,et al. Data Resource Profile: Clinical Practice Research Datalink (CPRD) , 2015, International journal of epidemiology.
[18] Ewout W Steyerberg,et al. Predictive accuracy of novel risk factors and markers: A simulation study of the sensitivity of different performance measures for the Cox proportional hazards regression model , 2015, Statistical methods in medical research.
[19] Ben Goldacre,et al. Prediction of Cardiovascular Risk Using Framingham, ASSIGN and QRISK2: How Well Do They Predict Individual Rather than Population Risk? , 2014, PloS one.
[20] A. Bauman,et al. Comparing population attributable risks for heart disease across the adult lifespan in women , 2014, British Journal of Sports Medicine.
[21] Jbs Board. Joint British Societies’ consensus recommendations for the prevention of cardiovascular disease (JBS3) , 2014, Heart.
[22] P. Glasziou,et al. Cardiovascular risk scores: qualitative study of how primary care practitioners understand and use them. , 2013, The British journal of general practice : the journal of the Royal College of General Practitioners.
[23] P Royston,et al. A simulation study of predictive ability measures in a survival model II: explained randomness and predictive accuracy , 2012, Statistics in medicine.
[24] Patrick Royston,et al. A simulation study of predictive ability measures in a survival model I: Explained variation measures , 2012, Statistics in medicine.
[25] M. Goldacre,et al. Determinants of the decline in mortality from acute myocardial infarction in England between 2002 and 2010: linked national database study , 2012, BMJ : British Medical Journal.
[26] Stef van Buuren,et al. MICE: Multivariate Imputation by Chained Equations in R , 2011 .
[27] M. Cowie,et al. UK stroke incidence, mortality and cardiovascular risk management 1999–2008: time-trend analysis from the General Practice Research Database , 2011, BMJ Open.
[28] E. Steyerberg,et al. [Regression modeling strategies]. , 2011, Revista espanola de cardiologia.
[29] M. Pencina,et al. On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data , 2011, Statistics in medicine.
[30] Carol Coupland,et al. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database , 2010, BMJ : British Medical Journal.
[31] Gary S Collins,et al. An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study , 2010, BMJ : British Medical Journal.
[32] Douglas G Altman,et al. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines , 2009, BMC medical research methodology.
[33] E. Steyerberg. Clinical Prediction Models , 2008, Statistics for Biology and Health.
[34] A. Sheikh,et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2 , 2008, BMJ : British Medical Journal.
[35] M. Schumacher,et al. Consistent Estimation of the Expected Brier Score in General Survival Models with Right‐Censored Event Times , 2006, Biometrical journal. Biometrische Zeitschrift.
[36] Patrick Royston,et al. Explained Variation for Survival Models , 2006 .
[37] M. Gonen,et al. Concordance probability and discriminatory power in proportional hazards regression , 2005 .
[38] John O'Quigley,et al. Explained randomness in proportional hazards models , 2005, Statistics in medicine.
[39] Patrick Royston,et al. A new measure of prognostic separation in survival data , 2004, Statistics in medicine.
[40] Sunil J Rao,et al. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .
[41] John O'Quigley,et al. Proportional hazards models with frailties and random effects , 2002, Statistics in medicine.
[42] P. Libby,et al. Inflammation and Atherosclerosis , 2002, Circulation.
[43] Shah Ebrahim,et al. Dietary fat intake and prevention of cardiovascular disease: systematic review , 2001, BMJ : British Medical Journal.
[44] Frank E. Harrell,et al. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .
[45] E Graf,et al. Assessment and comparison of prognostic classification schemes for survival data. , 1999, Statistics in medicine.
[46] F. Harrell,et al. Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .
[47] John T. Kent,et al. Measures of dependence for censored survival data , 1988 .
[48] E. Hofer. The Uncertainty Analysis of Model Results , 2018 .
[49] John P. A. Ioannidis,et al. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review , 2017, J. Am. Medical Informatics Assoc..
[50] K. Nanchahal,et al. The organisation and delivery of health improvement in general practice and primary care: a scoping study , 2015 .
[51] A. Benner. Multivariable Fractional Polynomials , 2010 .
[52] Northgate. Hospital Episode Statistics , 2006 .
[53] Hilde van der Togt,et al. Publisher's Note , 2003, J. Netw. Comput. Appl..