ArticleJournal of pharmacokinetics and pharmacodynamics2013
Cross-validation for nonlinear mixed effects models.
Article in Journal of pharmacokinetics and pharmacodynamics, 2013. 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.
- Establishing the Exposure-QT Relationship During Bedaquiline Treatment Using a Time-Varying Tuberculosis-Specific Correction Factor (QTcTBT).CPT: pharmacometrics & systems pharmacology · 2025Trial
- Polymorphic Expression of UGT1A9 is Associated with Variable Acetaminophen Glucuronidation in Neonates: A Population Pharmacokinetic and Pharmacogenetic Study.Clinical pharmacokinetics · 2018Trial
- Trial
- The predictive performance of the Marsh model for propofol target-controlled infusion during laparoscopic surgery with liver retraction.Journal of anesthesia · 2026Article
- Cognitive engagement induces area-specific fingerprints of dopamine, acetylcholine, serotonin, glutamate and GABA in prefrontal cortex and striatum.bioRxiv : the preprint server for biology · 2026Article
- Assessing the performance of QSP models: biology as the driver for validation.Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Article
- Exposure-Response Modeling to Support Dosing Selection for Phase IIb Development of Kukoamine B in Sepsis Patients.Frontiers in pharmacology · 2021Article
- Development and Evaluation of a Prediction Model for Ascertaining Rheumatic Heart Disease Status in Administrative Data.Clinical epidemiology · 2020Article
- Supplementing Claims Data with Electronic Medical Records to Improve Estimation and Classification of Rheumatoid Arthritis Disease Activity: A Machine Learning Approach.ACR open rheumatology · 2019Article
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
Abstract
Cross-validation is frequently used for model selection in a variety of applications. However, it is difficult to apply cross-validation to mixed effects models (including nonlinear mixed effects models or NLME models) due to the fact that cross-validation requires "out-of-sample" predictions of the outcome variable, which cannot be easily calculated when random effects are present. We describe two novel variants of cross-validation that can be applied to NLME models. One variant, where out-of-sample predictions are based on post hoc estimates of the random effects, can be used to select the overall structural model. Another variant, where cross-validation seeks to minimize the estimated random effects rather than the estimated residuals, can be used to select covariates to include in the model. We show that these methods produce accurate results in a variety of simulated data sets and apply them to two publicly available population pharmacokinetic data sets.
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What 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.