Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time

[1]  J. Dushoff,et al.  pcoxtime: Penalized Cox Proportional Hazard Model for Time-dependent Covariates , 2021, 2102.02297.

[2]  Arcot Sowmya,et al.  A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction , 2020, Scientific Reports.

[3]  J. Simonoff,et al.  Ensemble Methods for Survival Data with Time-Varying Covariates , 2020 .

[4]  C. Earle,et al.  Development and Validation of a Prognostic Survival Model With Patient-Reported Outcomes for Patients With Cancer , 2020, JAMA network open.

[5]  Dimitar Vassilev,et al.  Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies , 2019, Inf..

[6]  Ping Wang,et al.  Machine Learning for Survival Analysis , 2019, ACM Comput. Surv..

[7]  Payam Barnaghi,et al.  Learning from data to predict future symptoms of oncology patients , 2018, PloS one.

[8]  Uri Shaham,et al.  DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network , 2016, BMC Medical Research Methodology.

[9]  Chandan K. Reddy,et al.  Machine Learning for Survival Analysis: A Survey , 2017, ArXiv.

[10]  James E. Helmreich.  Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression and Survival Analysis (2nd Edition) , 2016 .

[11]  A. Beigzadeh,et al.  Machine learning models in breast cancer survival prediction. , 2016, Technology and health care : official journal of the European Society for Engineering and Medicine.

[12]  A. Agarwal,et al.  The accuracy of clinicians' predictions of survival in advanced cancer: a review. , 2016, Annals of palliative medicine.

[13]  K. Brazil,et al.  The Effect of Community-Based Specialist Palliative Care Teams on Place of Care. , 2016, Journal of palliative medicine.

[14]  Dimitrios I. Fotiadis,et al.  Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.

[15]  Laine Thomas,et al.  Tutorial: Survival Estimation for Cox Regression Models with Time-Varying Coe?cients Using SAS and R , 2014 .

[16]  Hemant Ishwaran,et al.  Random survival forests for competing risks. , 2014, Biostatistics.

[17]  Sunil R. Gupta,et al.  Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry , 2014, BMJ Open.

[18]  Thomas A Gerds,et al.  Estimating a time‐dependent concordance index for survival prediction models with covariate dependent censoring , 2013, Statistics in medicine.

[19]  Denis Larocque,et al.  A review of survival trees , 2011 .

[20]  E. Steyerberg,et al.  [Regression modeling strategies]. , 2011, Revista espanola de cardiologia.

[21]  C. Earle,et al.  Trajectory of performance status and symptom scores for patients with cancer during the last six months of life. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  Trevor Hastie,et al.  Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. , 2011, Journal of statistical software.

[23]  Sergio A. Alvarez,et al.  Machine learning of clinical performance in a pancreatic cancer database , 2010, Artif. Intell. Medicine.

[24]  Yvonne Freeh,et al.  An R and S–PLUS Companion to Applied Regression , 2004 .

[25]  Y. Ku,et al.  Predicting factors for survival of patients with unresectable pancreatic cancer: a management guideline. , 2003, Hepato-gastroenterology.

[26]  J. Finkelstein,et al.  How accurate are physicians' clinical predictions of survival and the available prognostic tools in estimating survival times in terminally ill cancer patients? A systematic review. , 2001, Clinical oncology (Royal College of Radiologists (Great Britain)).

[27]  Frank E. Harrell,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .

[28]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[29]  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 .

[30]  Paul D. Allison,et al.  Survival analysis using sas®: a practical guide , 1995 .

[31]  H. Baxter Williams,et al.  A Survey , 1992 .

[32]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[33]  R. Prentice,et al.  Commentary on Andersen and Gill's "Cox's Regression Model for Counting Processes: A Large Sample Study" , 1982 .

[34]  R. Gill,et al.  Cox's regression model for counting processes: a large sample study : (preprint) , 1982 .

[35]  D.,et al.  Regression Models and Life-Tables , 2022 .