ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- A multiobjective optimization approach to data assimilation for complex biological systems with sparse data.Mathematical biosciences · 2026Article
- Developing A Data Pipeline to Quantify Ventilator Waveforms.medRxiv : the preprint server for health sciences · 2025Article
- Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems.Mathematical biosciences · 2019Article
- Methodological variations in lagged regression for detecting physiologic drug effects in EHR data.Journal of biomedical informatics · 2018Article
- Biases in electronic health record data due to processes within the healthcare system: retrospective observational study.BMJ (Clinical research ed.) · 2018Observational
- High-fidelity phenotyping: richness and freedom from bias.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms.Journal of biomedical informatics · 2018Article
- The Association Between Ventilator Dyssynchrony, Delivered Tidal Volume, and Sedation Using a Novel Automated Ventilator Dyssynchrony Detection Algorithm.Critical care medicine · 2018Observational
- Yield and bias in defining a cohort study baseline from electronic health record data.Journal of biomedical informatics · 2018Article
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.
Funding
Abstract
Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.
Indexed as
Identifiers
28269874PMC5333294What 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.