ArticlePsychometrika2015
Rationale and Applications of Survival Tree and Survival Ensemble Methods.
Article in Psychometrika, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Establishing and validating models integrated with hematological biomarkers and clinical characteristics for the prognosis of non-esophageal squamous cell carcinoma patients.Annals of medicine · 2025Article
- Factors associated with survival of tuberculosis patients in Southeast Iran: comparison of stepwise cox regression, survival tree, and random survival forest.BMC public health · 2025Article
- AI-assisted exposure-response data analysis: Quantifying heterogeneous causal effects of exposures on survival times.Global epidemiology · 2025Article
- Clinical characteristics, prognosis, and predictive modeling in class IV ± V lupus nephritis.Frontiers in immunology · 2025Article
- Artificial intelligence in hepatocellular carcinoma screening: applications and challenges.Frontiers in medicine · 2025Review
- Development and Validation of a Machine Learning-Based Prognostic Model for IgA Nephropathy with Chronic Kidney Disease Stage 3 or 4.Kidney diseases (Basel, Switzerland) · 2024Article
- Recursive partitioning analysis for survival stratification and early imaging prediction of molecular biomarker in glioma patients.BMC cancer · 2024Article
- Development of machine learning models for patients in the high intrahepatic cholangiocarcinoma incidence age group.BMC geriatrics · 2024Article
- The role of VANGL2 in glioma oncogenesis and progression: insights into expression profiles and prognostic relevance.Frontiers in oncology · 2024Article
- Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients.Translational cancer research · 2023Article
- Comparison of models for stroke-free survival prediction in patients with CADASIL.Scientific reports · 2023Observational
- Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive.Scientific reports · 2023Article
- Development and visualization of a risk prediction model for metabolic syndrome: a longitudinal cohort study based on health check-up data in China.Frontiers in nutrition · 2023Article
- Decision trees: from efficient prediction to responsible AI.Frontiers in artificial intelligence · 2023Review
- Improvement of a prediction model for heart failure survival through explainable artificial intelligence.Frontiers in cardiovascular medicine · 2023Article
- Mixture survival trees for cancer risk classification.Lifetime data analysis · 2022Article
- Interval censored recursive forests.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
- Radiomics model of 18F-FDG PET/CT imaging for predicting disease-free survival of early-stage uterine cervical squamous cancer.Cancer biomarkers : section A of Disease markers · 2022Review
- Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomics: a combined analysis of two independent prospective European trials.European journal of nuclear medicine and molecular imaging · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Classification and Regression Trees (CART), and their successors-bagging and random forests, are statistical learning tools that are receiving increasing attention. However, due to characteristics of censored data collection, standard CART algorithms are not immediately transferable to the context of survival analysis. Questions about the occurrence and timing of events arise throughout psychological and behavioral sciences, especially in longitudinal studies. The prediction power and other key features of tree-based methods are promising in studies where an event occurrence is the outcome of interest. This article reviews existing tree algorithms designed specifically for censored responses as well as recently developed survival ensemble methods, and introduces available computer software. Through simulations and a practical example, merits and limitations of these methods are discussed. Suggestions are provided for practical use.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.