Evidence map›Paper›PMID 28887735›Full record

ArticleJournal of pharmacokinetics and pharmacodynamics2017

An automated sampling importance resampling procedure for estimating parameter uncertainty.

Anne-Gaëlle Dosne, Martin Bergstrand, Mats O Karlsson

Abstract read
In one paragraph

Article in Journal of pharmacokinetics and pharmacodynamics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed, 2 pooled it
–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

110 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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50 more citing papers are in PubMed but not listed here.

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

3 authors.

Anne-Gaëlle DosneDepartment of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Martin BergstrandDepartment of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Mats O KarlssonDepartment of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. mats.karlsson@farmbio.uu.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantifying the uncertainty around endpoints used for decision-making in drug development is essential. In nonlinear mixed-effects models (NLMEM) analysis, this uncertainty is derived from the uncertainty around model parameters. Different methods to assess parameter uncertainty exist, but scrutiny towards their adequacy is low. In a previous publication, sampling importance resampling (SIR) was proposed as a fast and assumption-light method for the estimation of parameter uncertainty. A non-iterative implementation of SIR proved adequate for a set of simple NLMEM, but the choice of SIR settings remained an issue. This issue was alleviated in the present work through the development of an automated, iterative SIR procedure. The new procedure was tested on 25 real data examples covering a wide range of pharmacokinetic and pharmacodynamic NLMEM featuring continuous and categorical endpoints, with up to 39 estimated parameters and varying data richness. SIR led to appropriate results after 3 iterations on average. SIR was also compared with the covariance matrix, bootstrap and stochastic simulations and estimations (SSE). SIR was about 10 times faster than the bootstrap. SIR led to relative standard errors similar to the covariance matrix and SSE. SIR parameter 95% confidence intervals also displayed similar asymmetry to SSE. In conclusion, the automated SIR procedure was successfully applied over a large variety of cases, and its user-friendly implementation in the PsN program enables an efficient estimation of parameter uncertainty in NLMEM.

Indexed as

Models, StatisticalNonlinear DynamicsUncertaintyDrug DiscoveryAsymptotic covariance matrixBootstrapConfidence intervalsNonlinear mixed-effects modelsParameter uncertaintySampling importance resampling

Identifiers

PMID28887735
PMCPMC5686280

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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.