ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2022
Efficient Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Identification of PIWI-interacting RNAs based models for lung adenocarcinoma early detection: a multicenter cohort study.Molecular biomedicine · 2025Article
- Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features.Journal of the American Statistical Association · 2025Article
- Semi-supervised Triply Robust Inductive Transfer Learning.Journal of the American Statistical Association · 2025Article
- EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.Annals of statistics · 2024Article
- A framework for evaluating clinical artificial intelligence systems without ground-truth annotations.Nature communications · 2024Article
- Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Semisupervised transfer learning for evaluation of model classification performance.Biometrics · 2024Article
- Semi-supervised calibration of noisy event risk (SCANER) with electronic health records.Journal of biomedical informatics · 2023Article
- Semisupervised Calibration of Risk with Noisy Event Times (SCORNET) using electronic health record data.Biostatistics (Oxford, England) · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- Efficient Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabeled data to improve estimation or prediction. However, current SSL literature focuses primarily on settings where labeled data is selected uniformly at random from the population of interest. Stratified sampling, while posing additional analytical challenges, is highly applicable to many real world problems. Moreover, no SSL methods currently exist for estimating the prediction performance of a fitted model when the labeled data is not selected uniformly at random. In this paper, we propose a two-step SSL procedure for evaluating a prediction rule derived from a working binary regression model based on the Brier score and overall misclassification rate under stratified sampling. In step I, we impute the missing labels via weighted regression with nonlinear basis functions to account for stratified sampling and to improve efficiency. In step II, we augment the initial imputations to ensure the consistency of the resulting estimators regardless of the specification of the prediction model or the imputation model. The final estimator is then obtained with the augmented imputations. We provide asymptotic theory and numerical studies illustrating that our proposals outperform their supervised counterparts in terms of efficiency gain. Our methods are motivated by electronic health record (EHR) research and validated with a real data analysis of an EHR-based study of diabetic neuropathy.
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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.