SynthesisProceedings of the National Academy of Sciences of the United States of America2018
Statistical tests and identifiability conditions for pooling and analyzing multisite datasets.
Synthesis in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.NPJ digital medicine · 2026Article
- A General Linear Mixed Effect Model to Infer Biomarker Correlations by Bridging Retrospectively Measured Data Across Multiple Studies.Statistics in medicine · 2025Article
- Abnormal subthalamic nucleus functional connectivity and machine learning classification in Parkinson's disease: a multisite functional magnetic resonance imaging study.Frontiers in aging neuroscience · 2025Article
- Overcoming Data Gaps in Life Course Epidemiology by Matching Across Cohorts.Epidemiology (Cambridge, Mass.) · 2024Article
- Recent methodological advances in federated learning for healthcare.Patterns (New York, N.Y.) · 2024Review
- Cross-scanner harmonization methods for structural MRI may need further work: A comparison study.NeuroImage · 2023Article
- Forward Operator Estimation in Generative Models with Kernel Transfer Operators.Proceedings of machine learning research · 2022Article
- Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2022Article
- Mitigating site effects in covariance for machine learning in neuroimaging data.Human brain mapping · 2022Article
- Learning Invariant Representations using Inverse Contrastive Loss.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2021Article
- Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease.Human brain mapping · 2020Article
- FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2020Article
- An international cohort study of birth outcomes associated with hospitalized acute respiratory infection during pregnancy.The Journal of infection · 2020Article
- PLCG2 protective variant p.P522R modulates tau pathology and disease progression in patients with mild cognitive impairment.Acta neuropathologica · 2020Article
- Article
- APP-derived peptides reflect neurodegeneration in frontotemporal dementia.Annals of clinical and translational neurology · 2019Article
- Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: A multi-resolution graph analysis.NeuroImage. Clinical · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between datasets, especially due to the distributional shifts in some predictors, may obfuscate real effects when datasets are combined. We present a rigorous statistical treatment of this problem and identify conditions where we can correct the distributional shift. We also provide an algorithm for the situation where the correction is identifiable. We analyze various properties of the framework for testing model fit, constructing confidence intervals, and evaluating consistency characteristics. Our technical development is motivated by Alzheimer's disease (AD) studies, and we present empirical results showing that our framework enables harmonizing of protein biomarkers, even when the assays across sites differ. Our contribution may, in part, mitigate a bottleneck that researchers face in clinical research when pooling smaller sized datasets and may offer benefits when the subjects of interest are difficult to recruit or when resources prohibit large single-site studies.
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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.