ArticleBiostatistics (Oxford, England)2015
Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models.
Article in Biostatistics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed, 42 citations in OpenAlex.
- Article
- A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.BMC medical research methodology · 2024Article
- Machine Learning-Based Survival Prediction Models for Progression-Free and Overall Survival in Advanced-Stage Hodgkin Lymphoma.JCO clinical cancer informatics · 2024Article
- Machine learning versus regression for prediction of sporadic pancreatic cancer.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2023Article
- Machine learning in medicine: what clinicians should know.Singapore medical journal · 2023Review
- Uncertainty in lung cancer stage for survival estimation via set-valued classification.Statistics in medicine · 2022Article
- Stacked survival models for residual lifetime data.BMC medical research methodology · 2022Article
- The relationship between the C-statistic and the accuracy of program-specific evaluations.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2019Article
- Optimizing predictive performance of criminal recidivism models using registration data with binary and survival outcomes.PloS one · 2019Article
- Estimating restricted mean treatment effects with stacked survival models.Statistics in medicine · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating characteristics in small sample sizes due to smaller variance than non-parametric estimators. Fundamentally, this is a bias-variance trade-off situation in that the sample size is not large enough to take advantage of the low bias of non-parametric estimation. Stacked survival models estimate an optimally weighted combination of models that can span parametric, semi-parametric, and non-parametric models by minimizing prediction error. An extensive simulation study demonstrates that stacked survival models consistently perform well across a wide range of scenarios by adaptively balancing the strengths and weaknesses of individual candidate survival models. In addition, stacked survival models perform as well as or better than the model selected through cross-validation. Finally, stacked survival models are applied to a well-known German breast cancer study.
Indexed as
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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.