ArticleJournal of the American Statistical Association2022
Individual Data Protected Integrative Regression Analysis of High-Dimensional Heterogeneous Data.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 38 citations in OpenAlex.
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- Robust angle-based transfer learning in high dimensions.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- A partially heterogeneous weighted fusion learning method for potential heterogeneous treatment effect in multi-site survival study.BMC medical research methodology · 2025Article
- Robust Inference for Federated Meta-Learning.Journal of the American Statistical Association · 2025Article
- Evolutionary algorithm-optimized feature fusion for accurate classification of shredded tobacco using multi-sensor data.Frontiers in plant science · 2025Article
- Centralized and Federated Models for the Analysis of Clinical Data.Annual review of biomedical data science · 2024Review
- Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis.Health data science · 2024Article
- Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.Journal of machine learning research : JMLR · 2024Article
- Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies.Journal of the American Statistical Association · 2024Article
- TARGETING UNDERREPRESENTED POPULATIONS IN PRECISION MEDICINE: A FEDERATED TRANSFER LEARNING APPROACH.The annals of applied statistics · 2023Article
- Article
- SurvMaximin: Robust federated approach to transporting survival risk prediction models.Journal of biomedical informatics · 2022Article
- Multisite learning of high-dimensional heterogeneous data with applications to opioid use disorder study of 15,000 patients across 5 clinical sites.Scientific reports · 2022Article
- Recent Developments in Privacy-Preserving Mining of Clinical Data.ACM/IMS transactions on data science · 2021Article
- Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints.Journal of machine learning research : JMLR · 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
3 authors at 3 institutions in 2 countries.
Funding
Abstract
Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the ultra high-dimensional setting. The challenge is even more pronounced when the individual-level data cannot be shared across studies, known as DataSHIELD contraint. Under sparse regression models that are assumed to be similar yet not identical across studies, we propose in this paper a novel integrative estimation procedure for data-Shielding High-dimensional Integrative Regression (SHIR). SHIR protects individual data through summary-statistics-based integrating procedure, accommodates between-study heterogeneity in both the covariate distribution and model parameters, and attains consistent variable selection. Theoretically, SHIR is statistically more efficient than the existing distributed approaches that integrate debiased LASSO estimators from the local sites. Furthermore, the estimation error incurred by aggregating derived data is negligible compared to the statistical minimax rate and SHIR is shown to be asymptotically equivalent in estimation to the ideal estimator obtained by sharing all data. The finite-sample performance of our method is studied and compared with existing approaches via extensive simulation settings. We further illustrate the utility of SHIR to derive phenotyping algorithms for coronary artery disease using electronic health records data from multiple chronic disease cohorts.
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Identifiers
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.