ArticlePharmaceutical research2026
Advances and Further Comparison of Software Tools for Fisher Information Matrix-Based Design Evaluation in Pharmacometrics.
Article in Pharmaceutical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimBefore conducting a clinical study involving pharmacometrics analyses using Non-Linear Mixed Effects Models (NLMEM), study design can be evaluated by computing the Fisher Information Matrix (FIM) via first-order model linearisation. A 2015 study showed that several dedicated tools provided consistent results, with block-diagonal FIM approximations aligning more closely with clinical trial simulations (CTS) than full matrix approaches. Since, some tools have evolved with new features like covariate modelling and inter-occasion variability (IOV), while others have been newly developed within software initially created for parameter estimation in NLMEM. Our first aim is to compare predictions from both older (PFIM, PopED) and newer tools (NONMEM$Design, MlxDesignEval and Pumas OptimalDesign), and secondly to compare available estimation software to derived empirical predictions from CTS.
methodsThe 2015 examples, involving an analytical pharmacokinetic (PK) model and a pharmacokinetic/pharmacodynamic model described by ODE, and new cases including a PK model with covariates and a cross over design with IOV were implemented in the five software. Predicted relative standard error (RSE) and D-criterion were computed. Empirical RSE and D-criterion were estimated from CTS with the different estimation tools.
resultsFor a given FIM approximation, both RSE and D-criterion were consistent across evaluation software. Results derived from block diagonal FIM were more aligned with CTS, for which results were comparable across estimation tools. Predictions were especially reliable for covariate effects and appropriately reflected the order of magnitude of IOV variances.
conclusionUsers can benefit from both reliable uncertainty prediction and parameter estimation regardless of their preferred software.
Indexed as
Identifiers
41840263What 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.