Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
暂无分享,去创建一个
Richard D Riley | Gary S Collins | Joie Ensor | Danielle L Burke | Frank E Harrell | F. Harrell | G. Collins | R. Riley | J. Ensor | K. Moons | K. Snell | D. Burke | Kym Ie Snell | Karel Gm Moons
[1] Richard D Riley,et al. Prediction of risk of recurrence of venous thromboembolism following treatment for a first unprovoked venous thromboembolism: systematic review, prognostic model and clinical decision rule, and economic evaluation. , 2016, Health technology assessment.
[2] L. Hooft,et al. A guide to systematic review and meta-analysis of prediction model performance , 2017, British Medical Journal.
[3] A Rogier T Donders,et al. Penalized maximum likelihood estimation to directly adjust diagnostic and prognostic prediction models for overoptimism: a clinical example. , 2004, Journal of clinical epidemiology.
[4] Paul C. Lambert,et al. Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model , 2011 .
[5] J. C. van Houwelingen,et al. Predictive value of statistical models , 1990 .
[6] Douglas G. Altman,et al. Adequate sample size for developing prediction models is not simply related to events per variable , 2016, Journal of clinical epidemiology.
[7] J. Kiefer,et al. Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator , 1956 .
[8] S Van Huffel,et al. A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data. , 2015, Journal of clinical epidemiology.
[9] R. Riley,et al. Development and validation of risk prediction model for venous thromboembolism in postpartum women: multinational cohort study , 2016, British Medical Journal.
[10] Patrick Royston,et al. Explained Variation for Survival Models , 2006 .
[11] Charles E McCulloch,et al. Relaxing the rule of ten events per variable in logistic and Cox regression. , 2007, American journal of epidemiology.
[12] David Hinkley,et al. Bootstrap Methods: Another Look at the Jackknife , 2008 .
[13] J. Copas,et al. Using regression models for prediction: shrinkage and regression to the mean , 1997, Statistical methods in medical research.
[14] D. Bloch,et al. A simple method of sample size calculation for linear and logistic regression. , 1998, Statistics in medicine.
[15] A. Sheikh,et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2 , 2008, BMJ : British Medical Journal.
[16] R. Blamey,et al. A prognostic index in primary breast cancer. , 1982, British Journal of Cancer.
[17] Douglas G. Altman,et al. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis , 2016, BMC Medical Research Methodology.
[18] Harry Hemingway,et al. Developing and validating a cardiovascular risk score for patients in the community with prior cardiovascular disease , 2017, Heart.
[19] P. Austin,et al. Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models , 2014, Statistical methods in medical research.
[20] Does my patient have chronic Chagas disease? Development and temporal validation of a diagnostic risk score. , 2016, Revista da Sociedade Brasileira de Medicina Tropical.
[21] B. Efron. Bootstrap Methods: Another Look at the Jackknife , 1979 .
[22] Yvonne Vergouwe,et al. Prognosis and prognostic research: Developing a prognostic model , 2009, BMJ : British Medical Journal.
[23] Ewout W Steyerberg,et al. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints , 2014, BMC Medical Research Methodology.
[24] L. Magee,et al. R 2 Measures Based on Wald and Likelihood Ratio Joint Significance Tests , 1990 .
[25] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[26] Patrick Royston,et al. Discrimination-based sample size calculations for multivariable prognostic models for time-to-event data , 2015, BMC Medical Research Methodology.
[27] Thomas Agoritsas,et al. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. , 2011, Journal of clinical epidemiology.
[28] P. Royston,et al. Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects , 2002, Statistics in medicine.
[29] J. Copas. Regression, Prediction and Shrinkage , 1983 .
[30] E. Steyerberg. Clinical Prediction Models , 2008, Statistics for Biology and Health.
[31] Janis Bormanis,et al. Value of assessment of pretest probability of deep-vein thrombosis in clinical management , 1997, The Lancet.
[32] Joseph R. Rausch,et al. Sample size planning for statistical power and accuracy in parameter estimation. , 2008, Annual review of psychology.
[33] J. Concato,et al. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. , 1995, Journal of clinical epidemiology.
[34] David R. Cox. The analysis of binary data , 1970 .
[35] J. Concato,et al. A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.
[36] Gary S Collins,et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.
[37] J. C. van Houwelingen,et al. Shrinkage and Penalized Likelihood as Methods to Improve Predictive Accuracy , 2001 .
[38] James E. Helmreich. Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression and Survival Analysis (2nd Edition) , 2016 .
[39] A. H. Feiveson,et al. Power by Simulation , 2002 .
[40] P Peduzzi,et al. Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. , 1995, Journal of clinical epidemiology.
[41] E. Steyerberg,et al. Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research , 2013, PLoS medicine.
[42] M Borenstein,et al. Planning for precision in survival studies. , 1994, Journal of clinical epidemiology.
[43] Harvey J Cohen,et al. An Overview of Variance Inflation Factors for Sample-Size Calculation , 2003, Evaluation & the health professions.
[44] Carol Coupland,et al. Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study , 2017, British Medical Journal.
[45] M Schumacher,et al. Sample size considerations for the evaluation of prognostic factors in survival analysis. , 2000, Statistics in medicine.
[46] John O'Quigley,et al. Explained randomness in proportional hazards models , 2005, Statistics in medicine.
[47] Gareth Ambler,et al. How to develop a more accurate risk prediction model when there are few events , 2015, BMJ : British Medical Journal.
[48] C.J.H. Mann,et al. Clinical Prediction Models: A Practical Approach to Development, Validation and Updating , 2009 .
[49] K. Anderson,et al. Cardiovascular disease risk profiles. , 1991, American heart journal.
[50] Econometric Modeling: A Likelihood Approach , 2007 .
[51] Gowri Raman,et al. Tufts PACE Clinical Predictive Model Registry: update 1990 through 2015 , 2017, Diagnostic and Prognostic Research.
[52] P W Lavori,et al. Sample-size calculations for the Cox proportional hazards regression model with nonbinary covariates. , 2000, Controlled clinical trials.
[53] Richard D Riley,et al. Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes , 2018, Statistics in medicine.
[54] I. Ellis,et al. The Nottingham prognostic index in primary breast cancer , 2005, Breast Cancer Research and Treatment.
[55] Patrick Royston,et al. A new measure of prognostic separation in survival data , 2004, Statistics in medicine.
[56] E. Steyerberg,et al. Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research , 2013, PLoS medicine.
[57] N. Nagelkerke,et al. A note on a general definition of the coefficient of determination , 1991 .
[58] O. Dekkers,et al. Predicting Mortality in Patients with Diabetes Starting Dialysis , 2014, PloS one.
[59] D. McFadden. Conditional logit analysis of qualitative choice behavior , 1972 .
[60] Maarten van Smeden,et al. Sample size for binary logistic prediction models: Beyond events per variable criteria , 2018, Statistical methods in medical research.
[61] M Gent,et al. Derivation of a Simple Clinical Model to Categorize Patients Probability of Pulmonary Embolism: Increasing the Models Utility with the SimpliRED D-dimer , 2000, Thrombosis and Haemostasis.