Evidence map›Paper›PMID 27604892›Full record

ArticlePharmaceutical research2016

Enhanced Method for Diagnosing Pharmacometric Models: Random Sampling from Conditional Distributions.

Marc Lavielle, Benjamin Ribba

Abstract read
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In one paragraph

Article in Pharmaceutical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed.

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  5. SCOUT: An Exploratory Approach to Scouting Dose-Relevant Covariates.CPT: pharmacometrics & systems pharmacology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Marc LavielleInria Saclay & CMAP, Ecole Polytechnique, University Paris-Saclay, Saint-Aubin, France.
Benjamin RibbaRoche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland. benjamin.ribba@roche.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeFor nonlinear mixed-effects pharmacometric models, diagnostic approaches often rely on individual parameters, also called empirical Bayes estimates (EBEs), estimated through maximizing conditional distributions. When individual data are sparse, the distribution of EBEs can "shrink" towards the same population value, and as a direct consequence, resulting diagnostics can be misleading.

methodsInstead of maximizing each individual conditional distribution of individual parameters, we propose to randomly sample them in order to obtain values better spread out over the marginal distribution of individual parameters.

resultsWe evaluated, through diagnostic plots and statistical tests, hypothesis related to the distribution of the individual parameters and show that the proposed method leads to more reliable results than using the EBEs. In particular, diagnostic plots are more meaningful, the rate of type I error is correctly controlled and its power increases when the degree of misspecification increases. An application to the warfarin pharmacokinetic data confirms the interest of the approach for practical applications.

conclusionsThe proposed method should be implemented to complement EBEs-based approach for increasing the performance of model diagnosis.

Indexed as

Models, BiologicalAnticoagulantsBayes TheoremComputer SimulationDrug DiscoveryHumansModels, StatisticalNonlinear DynamicsSoftwareWarfarinAnticoagulantsWarfarinmodel diagnosticsmodeling and simulationpharmacokinetics and pharmacodynamics

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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.