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

[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..