ArticleCPT: pharmacometrics & systems pharmacology2024
Using forest plots to interpret covariate effects in pharmacometric models.
Article in CPT: pharmacometrics & systems pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pharmacokinetics of IL-23p19 Inhibitors: A Systematic Review Across Immune-Mediated Inflammatory Diseases.Clinical pharmacokinetics · 2026Pooled it
- Population pharmacokinetics and exposure-response modelling of firsekibart (GenSci048) in patients with acute gout flare: Implications for fixed-dose optimization.British journal of clinical pharmacology · 2026Article
- Population Pharmacokinetic-Pharmacodynamic Analysis of Zolpidem in Healthy Volunteers: Covariate Contributions to Variability in CNS Responses.CPT: pharmacometrics & systems pharmacology · 2026Article
- Conditional Versus Unconditional Covariate Effects in Pharmacometric Models: Implications for Interpretation, Communication, and Reporting.CPT: pharmacometrics & systems pharmacology · 2026Article
- Recommended approaches for integration of population pharmacokinetic modelling with precision dosing in clinical practice.British journal of clinical pharmacology · 2025Review
- Predictive value of triglyceride-glucose index for the occurrence of acute respiratory failure in asthmatic patients of MIMIC-IV database.Scientific reports · 2024Observational
- Full random effects models (FREM): A practical usage guide.CPT: pharmacometrics & systems pharmacology · 2024Article
- Using forest plots to interpret covariate effects in pharmacometric models.CPT: pharmacometrics & systems pharmacology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The inclusion of covariates in pharmacometric models is important due to their ability to explain variability in drug exposure and response. Clear communication of the impact of covariates is needed to support informed decision making in clinical practice and in drug development. However, effectively conveying these effects to key stakeholders and decision makers can be challenging. Forest plots have been proposed to meet these communication needs. However, forest plots for the illustration of covariate effects in pharmacometrics are complex combinations of model predictions, uncertainty estimates, tabulated results, and reference lines and intervals. The purpose of this tutorial is to outline the aspects that influence the interpretation of forest plots, recommend best practices, and offer specific guidance for a clear and transparent communication of covariate effects.
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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.