ArticleCPT: pharmacometrics & systems pharmacology2025
A tutorial on pharmacometric Markov models.
Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Population Pharmacokinetic/Pharmacodynamic Modeling of Therapeutic Enzymes in Lysosomal Storage Diseases.Clinical pharmacokinetics · 2026Pooled it
- Learning Covariate Relations in Disease Progression Models Using Symbolic Neural Networks.CPT: pharmacometrics & systems pharmacology · 2026Article
- Discrete-time neural Markov models.BMC medical research methodology · 2026Article
- Cell State Transitions Drive the Evolution of Disease Progression in B-Lymphoblastic Leukemia.Cancer research communications · 2026Article
- Exposure-driven tolerance modeling of efpeglenatide-induced nausea or vomiting using repeated time-to-event analysis: implications for dose-escalation strategies.Frontiers in pharmacology · 2026Article
- Interplay between pharmacokinetics and immunogenicity of therapeutic proteins: stepwise development of a bidirectional joint pharmacokinetics-anti-drug antibodies model.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Historical Use of Markov Model and Posterior Predictive Checks in Pharmacometrics.CPT: pharmacometrics & systems pharmacology · 2025Article
- A tutorial on pharmacometric Markov models.CPT: pharmacometrics & systems pharmacology · 2025Article
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 Markov chain is a stochastic process in which the future value of a variable is conditionally independent of the past, given its present value. Data with Markovian features are characterized by: frequent observations relative to the expected changes in values, many consecutive same-category or similar-value observations at the individual level, and a positive correlation observed between the current and previous values for that variable. In drug development and clinical settings, the data available commonly present Markovian features and are increasingly often modeled using Markov elements or dedicated Markov models. This tutorial presents the main characteristics, evaluations, and applications of various Markov modeling approaches including the discrete-time Markov models (DTMM), continuous-time Markov models (CTMM), hidden Markov models, and item-response theory model with Markov sub-models. The tutorial has a specific emphasis on the use of DTMM and CTMM for modeling ordered-categorical data with Markovian features. Although the main body of this tutorial is written in a software-neutral manner, annotated NONMEM code for all key Markov models is included in the Supplementary Information.
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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.