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References from Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient‐Specific Survival Probabilities in Clinical Trial Data. Local targets link to admitted publications; unresolved targets remain external evidence.
Graphical Calibration Curves and the Integrated Calibration Index (ICI) for Survival Models
10.1002/sim.8570 · 2020 · External reference
Subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical Trials
10.18637/jss.v099.i14 · 2021 · External reference
Individual Risk Prediction: Comparing Random Forests With Cox Proportional‐Hazards Model by a Simulation Study
10.1002/bimj.202100380 · 2022 · External reference
Molecular‐Based Recursive Partitioning Analysis Model for Glioblastoma in the Temozolomide Era: A Correlative Analysis Based on NRG Oncology RTOG 0525
10.1001/jamaoncol.2016.6020 · 2017 · External reference
Generating Survival Times to Simulate Cox Proportional Hazards Models
10.1002/sim.2059 · 2005 · External reference
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10.1007/978-1-4613-3030-1_93
10.1007/978-1-4613-3030-1_93 · External reference
Hormone Therapy for Prostate Cancer: Results of the Veterans Administration Cooperative Urological Research Group Studies
1988 · External reference
The Choice of Treatment for Cancer Patients Based on Covariate Information
1980 · External reference
Prediction of Hypertension Using Traditional Regression and Machine Learning Models: A Systematic Review and Meta‐Analysis
10.1371/journal.pone.0266334 · 2022 · External reference
A Comparison of Machine Learning Algorithms and Traditional Regression‐Based Statistical Modeling for Predicting Hypertension Incidence in a Canadian Population
10.1038/s41598-022-27264-x · 2023 · External reference
Detection and Localization of Surgically Resectable Cancers With a Multi‐Analyte Blood Test
10.1126/science.aar3247 · 2018 · External reference
External Validation of Multivariable Prediction Models: A Systematic Review of Methodological Conduct and Reporting
10.1186/1471-2288-14-40 · 2014 · External reference
Developing Risk Prediction Models for Type 2 Diabetes: A Systematic Review of Methodology and Reporting
10.1186/1741-7015-9-103 · 2011 · External reference
Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction
10.1161/circulationaha.106.672402 · 2007 · External reference
Regression Models and Life‐Tables
10.1111/j.2517-6161.1972.tb00899.x · 1972 · External reference
Novel Head and Neck Cancer Survival Analysis Approach: Random Survival Forests Versus Cox Proportional Hazards Regression
10.1002/hed.21698 · 2012 · External reference
Comparison of the Tree‐Based Machine Learning Algorithms to Cox Regression in Predicting the Survival of Oral and Pharyngeal Cancers: Analyses Based on SEER Database
10.3390/cancers12102802 · 2020 · External reference
Improvements on Cross‐Validation: The 0.632+ Bootstrap Method
1997 · External reference
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Risk Factors Associated With Major Adverse Cardiac and Cerebrovascular Events Following Percutaneous Coronary Intervention: A 10‐Year Follow‐Up Comparing Random Survival Forest and Cox Proportional‐Hazards Model
10.1186/s12872-020-01834-1 · 2021 · External reference
Accuracy of Predicting Residual Disease and Disease Progression During Active Surveillance for Esophageal Cancer
10.1245/s10434-025-18531-y · 2026 · External reference
Opportunities and Challenges in Developing Risk Prediction Models with Electronic Health Records Data: A Systematic Review
10.1093/jamia/ocw042 · 2017 · External reference
Assessment and Comparison of Prognostic Classification Schemes for Survival Data
10.1002/(sici)1097-0258(19990915/30)18:17/18<2529::aid-sim274>3.0.co;2-5 · 1999 · External reference
10.5964/qcmb.14891
10.5964/qcmb.14891 · External reference
Proportional Hazards Tests and Diagnostics Based on Weighted Residuals
10.1093/biomet/81.3.515 · 1994 · External reference
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1985 · External reference
Developing and Validating Models to Predict Progression to Proliferative Diabetic Retinopathy
10.1016/j.xops.2023.100276 · 2023 · External reference
Evaluating the Yield of Medical Tests
10.1001/jama.1982.03320430047030 · 1982 · External reference
Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors
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Pitfalls of the Concordance Index for Survival Outcomes
10.1002/sim.9717 · 2023 · External reference
Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker
10.1111/j.0006-341x.2000.00337.x · 2004 · External reference
Time‐Dependence of Hazard Ratios for Prognostic Factors in Primary Breast Cancer
10.1023/a:1006133418245 · 1998 · External reference
The Majority of 922 Prediction Models Supporting Breast Cancer Decision‐Making Are at High Risk of Bias
10.1016/j.jclinepi.2022.10.016 · 2022 · External reference
Random Survival Forests
10.1214/08-aoas169 · 2008 · External reference
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Cardiovascular Effects of Hormone Therapy for Prostate Cancer
2015 · External reference
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
2008 · External reference
Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers
10.1158/1078-0432.ccr-18-3378 · 2019 · External reference
Deep Learning‐Based Survival Prediction of Oral Cancer Patients
10.1038/s41598-019-43372-7 · 2019 · External reference
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The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER‐Based Study
10.3390/cancers14194667 · 2022 · External reference
Personalizing prognosis in colorectal cancer: A systematic review of the quality and nature of clinical prognostic tools for survival outcomes
10.1002/jso.24774 · 2017 · External reference
Reporting performance of prognostic models in cancer: A review
10.1186/1741-7015-8-21 · 2010 · External reference
Assessing Performance and Clinical Usefulness in Prediction Models With Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models
10.7326/m22-0844 · 2023 · External reference
Risk Prediction of One‐Year Mortality in Patients With Cardiac Arrhythmias Using Random Survival Forest
10.1155/2015/303250 · 2015 · External reference
Machine Learning in Clinical Trials: A Primer With Applications to Neurology
10.1007/s13311-023-01384-2 · 2023 · External reference
Explainable Machine Learning Can Outperform Cox Regression Predictions and Provide Insights in Breast Cancer Survival
10.1038/s41598-021-86327-7 · 2021 · External reference
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration
10.7326/m14-0698 · 2015 · External reference
Risk Prediction Models: I. Development, Internal Validation, and Assessing the Incremental Value of a New (Bio)marker
10.1136/heartjnl-2011-301246 · 2012 · External reference
Using Simulation Studies to Evaluate Statistical Methods
10.1002/sim.8086 · 2019 · External reference
Artificial Intelligence Methods Available for Cancer Research
10.1007/s11684-024-1085-3 · 2024 · External reference
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The Comparisons of Random Survival Forests and Cox Regression Analysis With Simulation and an Application Related to Breast Cancer
10.1016/j.eswa.2008.10.023 · 2009 · External reference
Importance of Events per Independent Variable in Proportional Hazards Analysis I. Background, Goals, and General Strategy
10.1016/0895-4356(95)00048-8 · 1995 · External reference
Prognostic Models for Breast Cancer: A Systematic Review
10.1186/s12885-019-5442-6 · 2019 · External reference
A Comparison Study of Machine Learning (Random Survival Forest) and Classic Statistics (Cox Proportional Hazards) for Predicting Progression in High‐Grade Glioma After Proton And Carbon Ion Radiotherapy
10.3389/fonc.2020.551420 · 2020 · External reference
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A New Approach to Modelling Interaction Between Treatment and Continuous Covariates in Clinical Trials by Using Fractional Polynomials
10.1002/sim.1815 · 2004 · External reference
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10.1002/9781118150672
10.1002/9781118150672 · 2005 · External reference
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Minimum Sample Size for Developing a Multivariable Prediction Model: PART II ‐ Binary and Time‐to‐Event Outcomes
10.1002/sim.7992 · 2019 · External reference
Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit
10.1155/2016/7087053 · 2016 · External reference
Explainability of Random Survival Forests in Predicting Conversion Risk From Mild Cognitive Impairment to Alzheimer's Disease
10.1186/s40708-023-00211-w · 2023 · External reference
A Comparison of Machine Learning Methods for Survival Analysis of High‐Dimensional Clinical Data for Dementia Prediction
10.1038/s41598-020-77220-w · 2020 · External reference
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Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research
10.1371/journal.pmed.1001381 · 2013 · External reference
Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures
10.1097/ede.0b013e3181c30fb2 · 2010 · External reference
10.1007/978-1-4757-3294-8
10.1007/978-1-4757-3294-8 · 2000 · External reference
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Calibration: the Achilles Heel of Predictive Analytics
10.1186/s12916-019-1466-7 · 2019 · External reference
Traditional Statistical Methods for Evaluating Prediction Models Are Uninformative as to Clinical Value: Towards a Decision Analytic Framework
10.1053/j.seminoncol.2009.12.004 · 2010 · External reference
Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression
10.1093/aje/kwk052 · 2006 · External reference
Assessment of Predictive Performance in Incomplete Data by Combining Internal Validation and Multiple Imputation
10.1186/s12874-016-0239-7 · 2016 · External reference
Essential Guidelines for Computational Method Benchmarking
10.1186/s13059-019-1738-8 · 2019 · External reference
The Role of Machine Learning in Clinical Research: Transforming the Future of Evidence Generation
10.1186/s13063-021-05489-x · 2021 · External reference
Prediction of Coronary Heart Disease Using Risk Factor Categories
10.1161/01.cir.97.18.1837 · 1998 · External reference
Milestones in the Management of Prostate Cancer
10.1002/tre.796 · 2021 · External reference
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Untapped Potential of Multicenter Studies: A Review of Cardiovascular Risk Prediction Models Revealed Inappropriate Analyses and Wide Variation in Reporting
10.1186/s41512-019-0046-9 · 2019 · External reference
Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms
10.3389/fonc.2020.00330 · 2020 · External reference
Rationale and Applications of Survival Tree and Survival Ensemble Methods
10.1007/s11336-014-9413-1 · 2015 · External reference
