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Robin Genuer | Anthony Devaux | C'ecile Proust-Lima | Karine P'eres | K. Pérès | Robin Genuer | C. Proust-Lima | A. Devaux | R. Genuer
[1] Paul S Albert,et al. On Estimating the Relationship between Longitudinal Measurements and Time‐to‐Event Data Using a Simple Two‐Stage Procedure , 2009, Biometrics.
[2] H. Müller,et al. Functional Data Analysis for Sparse Longitudinal Data , 2005 .
[3] H. Putter,et al. Dynamic Prediction in Clinical Survival Analysis , 2011 .
[4] Hemant Ishwaran,et al. Random survival forests for competing risks. , 2014, Biostatistics.
[5] S. Keleş,et al. Sparse partial least squares regression for simultaneous dimension reduction and variable selection , 2010, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[6] J. Ware,et al. Random-effects models for longitudinal data. , 1982, Biometrics.
[7] H. V. Houwelingen. Dynamic Prediction by Landmarking in Event History Analysis , 2007 .
[8] By Shu Jiang,et al. Functional Ensemble Survival Tree: Dynamic Prediction of Alzheimer’s Disease Progression Accommodating Multiple Time-Varying Covariates , 2020, bioRxiv.
[9] M. J. van der Laan,et al. Statistical Applications in Genetics and Molecular Biology Super Learner , 2010 .
[10] Tianxi Cai,et al. On longitudinal prediction with time‐to‐event outcome: Comparison of modeling options , 2017, Biometrics.
[11] Hemant Ishwaran,et al. Evaluating Random Forests for Survival Analysis using Prediction Error Curves. , 2012, Journal of statistical software.
[12] Jean-François Dartigues,et al. Estimating and comparing time‐dependent areas under receiver operating characteristic curves for censored event times with competing risks , 2013, Statistics in medicine.
[13] Cécile Proust-Lima,et al. Shared random-effect models for the joint analysis of longitudinal and time-to-event data: application to the prediction of prostate cancer recurrence , 2014 .
[14] Anastasios A. Tsiatis,et al. Joint Modeling of Longitudinal and Time-to-Event Data : An Overview , 2004 .
[15] Myriam Maumy-Bertrand,et al. Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data , 2015, Bioinform..
[16] P. Grambsch,et al. Primary biliary cirrhosis: Prediction of short‐term survival based on repeated patient visits , 1994, Hepatology.
[17] B. Goldstein,et al. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges , 2016, European heart journal.
[18] Irwin Nazareth,et al. Landmark Models for Optimizing the Use of Repeated Measurements of Risk Factors in Electronic Health Records to Predict Future Disease Risk , 2018, American journal of epidemiology.
[19] Hemant Ishwaran,et al. Random Survival Forests , 2008, Wiley StatsRef: Statistics Reference Online.
[20] Uri Shaham,et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network , 2016, BMC Medical Research Methodology.
[21] P. Mecocci,et al. Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness , 2014, NeuroImage: Clinical.
[22] Benoit Liquet,et al. Estimation of extended mixed models using latent classes and latent processes: the R package lcmm , 2015, 1503.00890.
[23] J. Goeman. L1 Penalized Estimation in the Cox Proportional Hazards Model , 2009, Biometrical journal. Biometrische Zeitschrift.
[24] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[25] Cécile Proust-Lima,et al. Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach. , 2009, Biostatistics.
[26] M Maclure,et al. Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. , 2001, American journal of epidemiology.
[27] Cécile Proust-Lima,et al. Individual dynamic predictions using landmarking and joint modelling: Validation of estimators and robustness assessment , 2017, Statistical methods in medical research.
[28] D Commenges,et al. Mortality with dementia: results from a French prospective community-based cohort. , 2001, American journal of epidemiology.
[29] Xihong Lin,et al. Semiparametric Modeling of Longitudinal Measurements and Time‐to‐Event Data–A Two‐Stage Regression Calibration Approach , 2008, Biometrics.
[30] Cécile Proust-Lima,et al. Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time‐to‐event in presence of censoring and competing risks , 2015, Biometrics.
[31] Matthias Schmid,et al. A review of spline function procedures in R , 2019, BMC Medical Research Methodology.
[32] Susan Murray,et al. Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo‐observation approach , 2020, Statistics in medicine.
[33] Dimitris Rizopoulos,et al. Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time‐to‐Event Data , 2011, Biometrics.
[34] K. Lazaridis,et al. Primary biliary cirrhosis , 1998, Springer Netherlands.
[35] E. Polley,et al. Super Learner for Survival Data Prediction , 2020, The international journal of biostatistics.
[36] Angela M Wood,et al. The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study , 2016, Statistics in medicine.
[37] Trevor Hastie,et al. Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. , 2011, Journal of statistical software.
[38] Yongseok Park,et al. Real‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models , 2013, Biometrics.
[39] Ruth H. Keogh,et al. Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison , 2020, Journal of the Royal Statistical Society: Series A (Statistics in Society).