ArticleJournal of pharmacokinetics and pharmacodynamics2008
Non-Bayesian knowledge propagation using model-based analysis of data from multiple clinical studies.
Article in Journal of pharmacokinetics and pharmacodynamics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Informative study designs to identify true parameter-covariate relationships.Journal of pharmacokinetics and pharmacodynamics · 2009Article
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
The ultimate goal in drug development is to establish the manner of safe and efficacious administration to patients. To achieve this in an efficient way the information contained in the clinical studies should contribute to the increasing pool of accumulated knowledge. The aim of this simulation study is to investigate different knowledge-propagation strategies when the data is analysed using a model-based approach in NONMEM. Pharmacokinetic studies were simulated according to several scenarios of the underlying model and study design, including a population-optimal design based on analysis of a previous study. Five approaches with different degrees of knowledge propagation were investigated: analysing the studies pooled into one dataset, merging the results from analysing the studies separately, fitting a pre-specified model that has been selected from a previous study on either the most recent study or on the pooled dataset, or naïvely analysing the most recent study without any regards to any previous study. The approaches were evaluated on what model was selected (qualitative knowledge, investigated by stepwise covariate selection within NONMEM) as well as parameter precision (quantitative knowledge) and predictive performance of the model. Pooling all studies into one dataset is the best approach for identifying the correct model and obtaining good predictive performance and merging the results of separate analyses may perform almost as well. Fitting a pre-specified model on new data is fast, without selection bias, and sanctioned for model-based confirmatory analyses. However, fitting the same pre-specified model to all available data is still fast and can be expected to perform better in terms of predictive performance than the unbiased alternative. Using ED-optimal design of sample times and stratification of subjects from different subgroups is a successful strategy which allows sparse sampling and handles prior parameter uncertainty.
Indexed as
Identifiers
17990085What 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.