ArticleJournal of the American Statistical Association2014
Generalized Ordinary Differential Equation Models.
Article in Journal of the American Statistical Association, 2014. 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.
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Who cites it
10 citing papers in PubMed.
- A Practical Identifiability Criterion Leveraging Weak-Form Parameter Estimation.Bulletin of mathematical biology · 2026Article
- Structural and practical identifiability of within-host models of virus dynamics - a review.Current opinion in systems biology · 2025Article
- Article
- Using metabolic networks to predict cross-feeding and competition interactions between microorganisms.Microbiology spectrum · 2024Article
- A Joint estimation approach to sparse additive ordinary differential equations.Statistics and computing · 2022Article
- Using Differential Evolution to Design Optimal Experiments.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2020Article
- Age-Structured Population Modeling of HPV-related Cervical Cancer in Texas and US.Scientific reports · 2018Article
- Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection.International journal of molecular sciences · 2017Article
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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
Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The simulation study and application of our methods to an influenza viral dynamics study suggest that the proposed methods have a superior performance in terms of accuracy over the existing ODE model estimation approach and the extended smoothing-based (ESB) method.
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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.