ArticleCPT: pharmacometrics & systems pharmacology2022
An introduction to the full random effects model.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI.CPT: pharmacometrics & systems pharmacology · 2025Pooled it
- A Longitudinal Disease Progression Model Characterizing Endoscopic Response and Remission in Crohn's Disease.Journal of pharmacokinetics and pharmacodynamics · 2026Trial
- Evaluating Vaginal Microbiome Effects on Antiretroviral Exposure in the Female Genital Tract Using Pharmacometric and Machine Learning Approaches.Clinical pharmacology and therapeutics · 2026Article
- Assessing Covariate Clinical Relevance in High-Dimensional PK Analysis: A Comparison of SCM+, FFEM, and FREM Approaches.CPT: pharmacometrics & systems pharmacology · 2026Article
- Objective First, Method Second: Why the Estimand Definition Comes First in Pharmacometric Covariate Modeling.CPT: pharmacometrics & systems pharmacology · 2026Article
- Physiologically Motivated Sequential Population Modeling of Albumin Trends and Vedolizumab Pharmacokinetics for Pregnancy Dosing Regimen Optimization.Clinical pharmacology and therapeutics · 2026Article
- The Regularized Horseshoe for Covariate Selection Improves Convenience and Predictive Performance in Population PK/PD Models.CPT: pharmacometrics & systems pharmacology · 2026Article
- Systematic Review and Meta-Analysis of Risk Factors for Dehydration and the Development of a Predictive Scoring System.Healthcare (Basel, Switzerland) · 2025Review
- Shap-Cov: An Explainable Machine Learning Based Workflow for Rapid Covariate Identification in Population Modeling.CPT: pharmacometrics & systems pharmacology · 2025Article
- The impact of misspecified covariate models on inclusion and omission bias when using fixed effects and full random effects models.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- The composite governance mechanisms and sustainable economic performance of Pakistan's textile industry.PloS one · 2025Article
- A Model-Based Evaluation of Noninvasive Biomarkers to Reflect Histological Nonalcoholic Fatty Liver Disease Scores.Pharmaceutical research · 2025Observational
- Simulation-based evaluation of the Pharmpy Automatic Model Development tool for population pharmacokinetic modeling in early clinical drug development.CPT: pharmacometrics & systems pharmacology · 2024Article
- 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
- Item response theory analysis of daytime sleepiness as a symptom of obstructive sleep apnea.CPT: pharmacometrics & systems pharmacology · 2024Article
- Covariate modeling in pharmacometrics: General points for consideration.CPT: pharmacometrics & systems pharmacology · 2024Review
- Breastfeeding is associated with the intelligence of school-age children in Mexico.Maternal & child nutrition · 2023Article
- Operational characteristics of full random effects modelling ('frem') compared to stepwise covariate modelling ('scm').Journal of pharmacokinetics and pharmacodynamics · 2023Article
- Altered glucose-dependent secretion of glucagon and ACTH is associated with insulin resistance, assessed by population analysis.Endocrine connections · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The full random-effects model (FREM) is a method for determining covariate effects in mixed-effects models. Covariates are modeled as random variables, described by mean and variance. The method captures the covariate effects in estimated covariances between individual parameters and covariates. This approach is robust against issues that may cause reduced performance in methods based on estimating fixed effects (e.g., correlated covariates where the effects cannot be simultaneously identified in fixed-effects methods). FREM covariate parameterization and transformation of covariate data records can be used to alter the covariate-parameter relation. Four relations (linear, log-linear, exponential, and power) were implemented and shown to provide estimates equivalent to their fixed-effects counterparts. Comparisons between FREM and mathematically equivalent full fixed-effects models (FFEMs) were performed in original and simulated data, in the presence and absence of non-normally distributed and highly correlated covariates. These comparisons show that both FREM and FFEM perform well in the examined cases, with a slightly better estimation accuracy of parameter interindividual variability (IIV) in FREM. In addition, FREM offers the unique advantage of letting a single estimation simultaneously provide covariate effect coefficient estimates and IIV estimates for any subset of the examined covariates, including the effect of each covariate in isolation. Such subsets can be used to apply the model across data sources with different sets of available covariates, or to communicate covariate effects in a way that is not conditional on other covariates.
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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.