ArticleArchives of toxicology2023
Guidance for statistical design and analysis of toxicological dose-response experiments, based on a comprehensive literature review.
Article in Archives of toxicology, 2023. 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.
- Identification of Changes in Gene Expression.Biometrical journal. Biometrische Zeitschrift · 2026Article
- A review of machine learning in toxicology: current practices and reporting gaps.Archives of toxicology · 2026Review
- Simple, robust and near optimal designs for the estimation of log-logistic dose response functions.Archives of toxicology · 2026Article
- Analysis of rat toxicology studies: statistical agreement between virtual and concurrent controls in detecting treatment effects on liver enzymes.Frontiers in pharmacology · 2026Article
- Automated computer vision and dose-response modeling improve throughput and accuracy of an ex vivo functional precision medicine platform.Scientific reports · 2025Article
- From chemistry to targeted cancer therapy: potent benzimidazole-1,2,3-triazole hybrid-loaded nanogels against breast and colorectal malignancies.RSC advances · 2025Article
- Article
- Automated Computer Vision and Dose-Response Modeling Improve Throughput and Accuracy of anResearch square · 2025Article
- Overcoming Model Uncertainty - How Equivalence Tests Can Benefit From Model Averaging.Statistics in medicine · 2025Article
- Design of optimal concentrations for in vitro cytotoxicity experiments.Archives of toxicology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
The analysis of dose-response, concentration-response, and time-response relationships is a central component of toxicological research. A major decision with respect to the statistical analysis is whether to consider only the actually measured concentrations or to assume an underlying (parametric) model that allows extrapolation. Recent research suggests the application of modelling approaches for various types of toxicological assays. However, there is a discrepancy between the state of the art in statistical methodological research and published analyses in the toxicological literature. The extent of this gap is quantified in this work using an extensive literature review that considered all dose-response analyses published in three major toxicological journals in 2021. The aspects of the review include biological considerations (type of assay and of exposure), statistical design considerations (number of measured conditions, design, and sample sizes), and statistical analysis considerations (display, analysis goal, statistical testing or modelling method, and alert concentration). Based on the results of this review and the critical assessment of three selected issues in the context of statistical research, concrete guidance for planning, execution, and analysis of dose-response studies from a statistical viewpoint is proposed.
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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.