ArticleBiostatistics (Oxford, England)2016
Sparse meta-analysis with high-dimensional data.
Article in Biostatistics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Melody: meta-analysis of microbiome association studies for discovering generalizable microbial signatures.Genome biology · 2025Pooled it
- Sparse-selective quantization for real-time cyber threat detection in large-scale networks.PloS one · 2026Article
- Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding.Lifetime data analysis · 2025Article
- A partially heterogeneous weighted fusion learning method for potential heterogeneous treatment effect in multi-site survival study.BMC medical research methodology · 2025Article
- Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.Journal of machine learning research : JMLR · 2024Article
- CEDAR: communication efficient distributed analysis for regressions.Biometrics · 2023Article
- Statistical Inference for High-Dimensional Pathway Analysis with Multiple Responses.Computational statistics & data analysis · 2022Article
- Individual Data Protected Integrative Regression Analysis of High-Dimensional Heterogeneous Data.Journal of the American Statistical Association · 2022Article
- Meta-Analyzing Multiple Omics Data With Robust Variable Selection.Frontiers in genetics · 2021Article
- An adaptive test for meta-analysis of rare variant association studies.Genetic epidemiology · 2020Article
- Article
- Meta-analysis in the era of big data.Clinical rheumatology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Meta-analysis plays an important role in summarizing and synthesizing scientific evidence derived from multiple studies. With high-dimensional data, the incorporation of variable selection into meta-analysis improves model interpretation and prediction. Existing variable selection methods require direct access to raw data, which may not be available in practical situations. We propose a new approach, sparse meta-analysis (SMA), in which variable selection for meta-analysis is based solely on summary statistics and the effect sizes of each covariate are allowed to vary among studies. We show that the SMA enjoys the oracle property if the estimated covariance matrix of the parameter estimators from each study is available. We also show that our approach achieves selection consistency and estimation consistency even when summary statistics include only the variance estimators or no variance/covariance information at all. Simulation studies and applications to high-throughput genomics studies demonstrate the usefulness of our approach.
Indexed as
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.