ArticleBiometrical journal. Biometrische Zeitschrift2024
Penalized Regression Methods With Modified Cross-Validation and Bootstrap Tuning Produce Better Prediction Models.
Article in Biometrical journal. Biometrische Zeitschrift, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for infection after kidney transplantation: a systematic review.Frontiers in medicine · 2026Pooled it
- Immunosuppression Outweighs Clinical AEIOU Heuristics in Predicting Disease Progression of Merkel Cell Carcinoma: A Retrospective Cohort Study.Acta dermato-venereologica · 2026Article
- Elevated asprosin in hypertension: evidence from an exploratory case-control study.Scientific reports · 2026Article
- A Review of High-Throughput Optical Sensors for Food Detection Based on Machine Learning.Foods (Basel, Switzerland) · 2026Review
- An explainable artificial intelligence framework for clinical decision support in stroke discharge planning.PloS one · 2026Article
- Prediction of local recurrence in cohorts of head and neck cancer patients after intensity-modulated radiation therapy based on CT radiomics: A double-center observation study.Translational oncology · 2026Article
- Tuning Matters: Comparing Lambda Optimization Approaches for Ridge Regression in Genomic Prediction.Genes · 2025Article
- Family composition, income, and healthy diet in rural China: evidence from three provinces.Frontiers in nutrition · 2025Article
- Refining penalized ridge regression: a novel method for optimizing the regularization parameter in genomic prediction.G3 (Bethesda, Md.) · 2024Article
- An evaluation of sample size requirements for developing risk prediction models with binary outcomes.BMC medical research methodology · 2024Article
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.
Funding
Abstract
Risk prediction models fitted using maximum likelihood estimation (MLE) are often overfitted resulting in predictions that are too extreme and a calibration slope (CS) less than 1. Penalized methods, such as Ridge and Lasso, have been suggested as a solution to this problem as they tend to shrink regression coefficients toward zero, resulting in predictions closer to the average. The amount of shrinkage is regulated by a tuning parameter,
Indexed as
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What OpenQuestion holds
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.