Published in Eur J Epidemiol on November 20, 2012
The Rotterdam Study: 2016 objectives and design update. Eur J Epidemiol (2015) 1.61
Net reclassification improvement: a link between statistics and clinical practice. Eur J Epidemiol (2013) 0.93
Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers. Clin J Am Soc Nephrol (2014) 0.82
The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity. PLoS One (2013) 0.81
Reclassification of genetic-based risk predictions as GWAS data accumulate. Genome Med (2016) 0.79
Risk categorization for calculating net reclassification improvement. Eur J Epidemiol (2013) 0.75
Reconsidering lactate as a sepsis risk biomarker. PLoS One (2017) 0.75
Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation (2002) 76.93
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics (1988) 70.41
Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med (2008) 57.29
Prediction of coronary heart disease using risk factor categories. Circulation (1998) 48.81
Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation (2004) 23.07
Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J (2003) 17.80
Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med (2010) 14.49
Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology (2010) 14.07
Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol (2004) 12.03
Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making (2006) 11.83
Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ (2007) 8.87
Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med (2009) 7.62
Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Münster (PROCAM) study. Circulation (2002) 6.20
Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med (2006) 5.17
An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care (2007) 4.14
The numerical measure of the success of predictions. Science (1884) 3.33
Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst (2010) 2.82
Several methods to assess improvement in risk prediction models: extension to survival analysis. Stat Med (2010) 2.43
Improvement of risk prediction by genomic profiling: reclassification measures versus the area under the receiver operating characteristic curve. Am J Epidemiol (2010) 2.06
Recruitment procedures of EPIC-Germany. European Investigation into Cancer and Nutrition. Ann Nutr Metab (1999) 2.05
Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med (2011) 2.03
Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev (2011) 2.01
Performance of reclassification statistics in comparing risk prediction models. Biom J (2011) 1.63
Misuse of DeLong test to compare AUCs for nested models. Stat Med (2012) 1.54
Assessing the incremental value of diagnostic and prognostic markers: a review and illustration. Eur J Clin Invest (2011) 1.46
Net reclassification improvement and decision theory. Stat Med (2009) 1.41
Use of reclassification for assessment of improved prediction: an empirical evaluation. Int J Epidemiol (2011) 1.11
Useful tests of usefulness of new risk factors: tools for assessing reclassification and discrimination. Scand J Public Health (2011) 1.11
[Performance measures for prediction models and markers: evaluation of predictions and classifications]. Rev Esp Cardiol (2011) 0.91
Comments on 'Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers' by M. J. Pencina, R. B. D'Agostino, Sr. and E. W. Steyerberg. Stat Med (2011) 0.86
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet (2010) 23.08
A randomized trial of intraarterial treatment for acute ischemic stroke. N Engl J Med (2014) 18.12
Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med (2010) 14.49
Intake of sugar-sweetened beverages and weight gain: a systematic review. Am J Clin Nutr (2006) 13.19
Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet (2010) 7.94
Lung cancer susceptibility locus at 5p15.33. Nat Genet (2008) 5.99
Red meat consumption and mortality: results from 2 prospective cohort studies. Arch Intern Med (2012) 4.72
Predictive testing for complex diseases using multiple genes: fact or fiction? Genet Med (2006) 4.48
Meat, fish, and colorectal cancer risk: the European Prospective Investigation into cancer and nutrition. J Natl Cancer Inst (2005) 4.45
Body size and risk of colon and rectal cancer in the European Prospective Investigation Into Cancer and Nutrition (EPIC). J Natl Cancer Inst (2006) 4.17
Healthy living is the best revenge: findings from the European Prospective Investigation Into Cancer and Nutrition-Potsdam study. Arch Intern Med (2009) 4.15
An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care (2007) 4.14
Fruit and vegetable intake and overall cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC). J Natl Cancer Inst (2010) 3.98
Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med (2011) 3.94
Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol (2004) 3.85
Adiponectin and protection against type 2 diabetes mellitus. Lancet (2003) 3.79
Risk of esophageal adenocarcinoma and mortality in patients with Barrett's esophagus: a systematic review and meta-analysis. Clin Gastroenterol Hepatol (2009) 3.66
Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction. Am J Clin Nutr (2004) 3.61
Dietary patterns and risk of mortality from cardiovascular disease, cancer, and all causes in a prospective cohort of women. Circulation (2008) 3.56
Dietary fibre in food and protection against colorectal cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC): an observational study. Lancet (2003) 3.53
Modified Mediterranean diet and survival: EPIC-elderly prospective cohort study. BMJ (2005) 3.48
Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis. Am J Clin Nutr (2011) 3.27
The association of pattern of lifetime alcohol use and cause of death in the European prospective investigation into cancer and nutrition (EPIC) study. Int J Epidemiol (2013) 3.23
Two new Loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and german study groups. PLoS Genet (2010) 3.21
Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology (2009) 3.20
A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension. Eur Heart J (2011) 3.14
Imputation is beneficial for handling missing data in predictive models. J Clin Epidemiol (2007) 3.08
Inflammatory and metabolic biomarkers and risk of liver and biliary tract cancer. Hepatology (2014) 3.06
Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study. Ann Intern Med (2012) 3.05
Total extraperitoneal inguinal hernia repair compared with Lichtenstein (the LEVEL-Trial): a randomized controlled trial. Ann Surg (2010) 3.02
Consumption of vegetables and fruits and risk of breast cancer. JAMA (2005) 3.02
Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide. Ann Intern Med (2014) 3.00
Prediction of outcome in traumatic brain injury with computed tomographic characteristics: a comparison between the computed tomographic classification and combinations of computed tomographic predictors. Neurosurgery (2005) 2.97
The central melanocortin system directly controls peripheral lipid metabolism. J Clin Invest (2007) 2.97
Genome-wide association study of renal cell carcinoma identifies two susceptibility loci on 2p21 and 11q13.3. Nat Genet (2010) 2.96
Body size and breast cancer risk: findings from the European Prospective Investigation into Cancer And Nutrition (EPIC). Int J Cancer (2004) 2.96
Fruit, vegetables, and colorectal cancer risk: the European Prospective Investigation into Cancer and Nutrition. Am J Clin Nutr (2009) 2.93
Fruit and vegetable intake and the risk of stomach and oesophagus adenocarcinoma in the European Prospective Investigation into Cancer and Nutrition (EPIC-EURGAST). Int J Cancer (2006) 2.89
Meat consumption and prospective weight change in participants of the EPIC-PANACEA study. Am J Clin Nutr (2010) 2.87
Evaluation of human papillomavirus antibodies and risk of subsequent head and neck cancer. J Clin Oncol (2013) 2.87
Systematic review of guidelines on cardiovascular risk assessment: Which recommendations should clinicians follow for a cardiovascular health check? Arch Intern Med (2010) 2.82
Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements. J Clin Epidemiol (2004) 2.77
Prediction of MLH1 and MSH2 mutations in Lynch syndrome. JAMA (2006) 2.73
A clinical prognostic scoring system for Guillain-Barré syndrome. Lancet Neurol (2007) 2.73
Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med (2015) 2.71
Hepatocellular carcinoma risk factors and disease burden in a European cohort: a nested case-control study. J Natl Cancer Inst (2011) 2.63
Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes (2012) 2.62
Prognosis and clinical trial design in traumatic brain injury: the IMPACT study. J Neurotrauma (2007) 2.57
Smoking and risk for amyotrophic lateral sclerosis: analysis of the EPIC cohort. Ann Neurol (2009) 2.56
Multivariable prognostic analysis in traumatic brain injury: results from the IMPACT study. J Neurotrauma (2007) 2.54
Serum sex steroids in premenopausal women and breast cancer risk within the European Prospective Investigation into Cancer and Nutrition (EPIC). J Natl Cancer Inst (2005) 2.53
Fatty acid composition of plasma phospholipids and risk of prostate cancer in a case-control analysis nested within the European Prospective Investigation into Cancer and Nutrition. Am J Clin Nutr (2008) 2.53
Patient age and outcome following severe traumatic brain injury: an analysis of 5600 patients. J Neurosurg (2003) 2.51
Genome-wide association study identifies new prostate cancer susceptibility loci. Hum Mol Genet (2011) 2.51
Coronary calcium score improves classification of coronary heart disease risk in the elderly: the Rotterdam study. J Am Coll Cardiol (2010) 2.50
Cigarette smoking, environmental tobacco smoke exposure and pancreatic cancer risk in the European Prospective Investigation into Cancer and Nutrition. Int J Cancer (2010) 2.47
Fiber and magnesium intake and incidence of type 2 diabetes: a prospective study and meta-analysis. Arch Intern Med (2007) 2.47
Combined anatomical and clinical factors for the long-term risk stratification of patients undergoing percutaneous coronary intervention: the Logistic Clinical SYNTAX score. Eur Heart J (2012) 2.43
Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol (2005) 2.42
Fruit and vegetable intake and mortality from ischaemic heart disease: results from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Heart study. Eur Heart J (2011) 2.42
GOAT links dietary lipids with the endocrine control of energy balance. Nat Med (2009) 2.42
Plasma fetuin-A levels and the risk of type 2 diabetes. Diabetes (2008) 2.41
Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol (2012) 2.39
Reclassification calculations for persons with incomplete follow-up. Ann Intern Med (2010) 2.35
Plasma selenium concentration and prostate cancer risk: results from the European Prospective Investigation into Cancer and Nutrition (EPIC). Am J Clin Nutr (2008) 2.35
The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases. Genet Med (2007) 2.34
Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ (2013) 2.31
Validity of prognostic models: when is a model clinically useful? Semin Urol Oncol (2002) 2.29
Reporting and methods in clinical prediction research: a systematic review. PLoS Med (2012) 2.26
Stent versus gastrojejunostomy for the palliation of gastric outlet obstruction: a systematic review. BMC Gastroenterol (2007) 2.26
Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. PLoS Med (2013) 2.23
Clinical usefulness of the Framingham cardiovascular risk profile beyond its statistical performance: the Tehran Lipid and Glucose Study. Am J Epidemiol (2012) 2.20
Consuming fructose-sweetened beverages increases body adiposity in mice. Obes Res (2005) 2.18
Patients with Barrett's esophagus perceive their risk of developing esophageal adenocarcinoma as low. Gastrointest Endosc (2007) 2.18
Air pollution and risk of lung cancer in a prospective study in Europe. Int J Cancer (2006) 2.17
Consumption of trans fatty acids is related to plasma biomarkers of inflammation and endothelial dysfunction. J Nutr (2005) 2.17
Baseline characteristics and statistical power in randomized controlled trials: selection, prognostic targeting, or covariate adjustment? Crit Care Med (2009) 2.16
The effect of study arm on prostate cancer treatment in the large screening trial ERSPC. Int J Cancer (2010) 2.15
Tbc1d1 mutation in lean mouse strain confers leanness and protects from diet-induced obesity. Nat Genet (2008) 2.14
Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med (2013) 2.13
Regional differences in patient characteristics, case management, and outcomes in traumatic brain injury: experience from the tirilazad trials. J Neurosurg (2002) 2.12
Associations of diabetes mellitus with total life expectancy and life expectancy with and without cardiovascular disease. Arch Intern Med (2007) 2.12
Colorectal neoplasia in veterans is associated with Barrett's esophagus but not with proton-pump inhibitor or aspirin/NSAID use. Gastrointest Endosc (2006) 2.10
IMPACT database of traumatic brain injury: design and description. J Neurotrauma (2007) 2.10
Systematic review of guidelines on abdominal aortic aneurysm screening. J Vasc Surg (2011) 2.08
Plasma fetuin-a levels and the risk of myocardial infarction and ischemic stroke. Circulation (2008) 2.08
Single-dose brachytherapy versus metal stent placement for the palliation of dysphagia from oesophageal cancer: multicentre randomised trial. Lancet (2004) 2.06
Design and analysis of phase III trials with ordered outcome scales: the concept of the sliding dichotomy. J Neurotrauma (2005) 2.06