ArticleBioinformatics advances2023
LongDat: an R package for covariate-sensitive longitudinal analysis of high-dimensional data.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 13 citations in OpenAlex.
- Gut microbiota responses to bariatric surgery are associated with metabolic outcomes and type 2 diabetes remission.Nature metabolism · 2026Trial
- MixedBayes: An R Package for Longitudinal Gene-Environment Interaction Analysis Using Robust Sparse Bayesian Mixed Models.Entropy (Basel, Switzerland) · 2026Article
- metadeconfoundR: Covariate analysis of high-dimensional cross-sectional omics data.Bioinformatics advances · 2026Article
- Article
- Antibiotic-perturbed microbiota and the role of probiotics.Nature reviews. Gastroenterology & hepatology · 2025Review
- Circulating Extracellular Vesicles as Putative Mediators of Cardiovascular Disease in Paediatric Chronic Kidney Disease.Journal of extracellular vesicles · 2025Article
- Article
- Sex hormone-dependent host-microbiome interactions and cardiovascular risk (XCVD): design of a longitudinal multi-omics cohort study.BMJ open · 2025Observational
- Fasting elicits gut microbiome signature changes that extend to type 1 diabetes patients.Frontiers in endocrinology · 2025Article
- Alterations in bile acid kinetics after bariatric surgery in patients with obesity with or without type 2 diabetes.EBioMedicine · 2024Article
- Airway microbiome signature accurately discriminatesiScience · 2024Article
- A clinical protocol for a German birth cohort study of the Maturation of Immunity Against respiratory viral Infections (MIAI).Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: We introduce LongDat, an R package that analyzes longitudinal multivariable (cohort) data while simultaneously accounting for a potentially large number of covariates. The primary use case is to differentiate direct from indirect effects of an intervention (or treatment) and to identify covariates (potential mechanistic intermediates) in longitudinal data. LongDat focuses on analyzing longitudinal microbiome data, but its usage can be expanded to other data types, such as binary, categorical and continuous data. We tested and compared LongDat with other tools (i.e. MaAsLin2, ANCOM, lgpr and ZIBR) on both simulated and real data. We showed that LongDat outperformed these tools in accuracy, runtime and memory cost, especially when there were multiple covariates. The results indicate that the LongDat R package is a computationally efficient and low-memory-cost tool for longitudinal data with multiple covariates and facilitates robust biomarker searches in high-dimensional datasets. Availability and implementation: The R package LongDat is available on CRAN (https://cran.r-project.org/web/packages/LongDat/) and GitHub (https://github.com/CCY-dev/LongDat). Supplementary information: Supplementary data are available at
Identifiers
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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.