2018
Smoking and Lung Cancer Mortality in the United States From 2015 to 2065: A Comparative Modeling Approach.
Jeon J, Holford TR, Levy DT, Feuer EJ, Cao P, Tam J, Clarke L, Clarke J, Kong CY, Meza R. Smoking and Lung Cancer Mortality in the United States From 2015 to 2065: A Comparative Modeling Approach. Annals Of Internal Medicine 2018, 169: 684-693. PMID: 30304504, PMCID: PMC6242740, DOI: 10.7326/m18-1250.Peer-Reviewed Original ResearchConceptsLung cancer mortalityTobacco control effortsCancer mortalityLung cancer ratesLung cancerCancer ratesLung cancer burdenLung cancer deathsLung cancer screeningSmoking-related diseasesU.S. populationNational Cancer InstituteLonger life expectancyCancer burdenSmoking patternsCancer deathCessation effortsCancer screeningTobacco useCancer InstituteSmokingAdditional preventionNatural historyMortalityCancer
1996
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi P, Concato J, Kemper E, Holford T, Feinstein A. A simulation study of the number of events per variable in logistic regression analysis. Journal Of Clinical Epidemiology 1996, 49: 1373-1379. PMID: 8970487, DOI: 10.1016/s0895-4356(96)00236-3.Peer-Reviewed Original Research
1995
Importance of events per independent variable in proportional hazards regression analysis II. Accuracy and precision of regression estimates
Peduzzi P, Concato J, Feinstein A, Holford T. Importance of events per independent variable in proportional hazards regression analysis II. Accuracy and precision of regression estimates. Journal Of Clinical Epidemiology 1995, 48: 1503-1510. PMID: 8543964, DOI: 10.1016/0895-4356(95)00048-8.Peer-Reviewed Original ResearchImportance of events per independent variable in proportional hazards analysis I. Background, goals, and general strategy
Concato J, Peduzzi P, Holford T, Feinstein A. Importance of events per independent variable in proportional hazards analysis I. Background, goals, and general strategy. Journal Of Clinical Epidemiology 1995, 48: 1495-1501. PMID: 8543963, DOI: 10.1016/0895-4356(95)00510-2.Peer-Reviewed Original ResearchMeSH KeywordsComputer SimulationConnecticutCoronary Artery BypassCoronary DiseaseHumansMonte Carlo MethodProportional Hazards ModelsRisk FactorsSurvival Rate