ArticleThe AAPS journal2017
A Minimal Continuous-Time Markov Pharmacometric Model.
Article in The AAPS journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed, 21 citations in OpenAlex.
- PK/PD modeling links accelerated resolution of COVID-19-related clinical symptoms to SARS-CoV-2 viral load reduction in patients following treatment with Bamlanivimab alone or Bamlanivimab and Etesevimab together.CPT: pharmacometrics & systems pharmacology · 2022Trial
- Improved Decision-Making Confidence Using Item-Based Pharmacometric Model: Illustration with a Phase II Placebo-Controlled Trial.The AAPS journal · 2021Trial
- An Innovative Pharmacometric Approach for the Simultaneous Analysis of Frequency, Duration and Severity of Migraine Events.Pharmaceutical research · 2020Trial
- PKPD Modeling and Dosing Considerations in Advanced Ovarian Cancer Patients Treated with Cisplatin-Based Intraoperative Intraperitoneal Chemotherapy.The AAPS journal · 2020Trial
- Simulation Framework to Investigate Efficacy and Ocular Safety of Belantamab Mafodotin Combinations in Relapsed/Refractory Multiple Myeloma.CPT: pharmacometrics & systems pharmacology · 2026Article
- A Novel Two-Part Mixture Model for the Incidence and Time Course of Cytokine Release Syndrome After Elranatamab Dosing in Multiple Myeloma Patients.Clinical pharmacology and therapeutics · 2025Article
- A tutorial on pharmacometric Markov models.CPT: pharmacometrics & systems pharmacology · 2025Article
- A Markov model of fibrosis development in nonalcoholic fatty liver disease predicts fibrosis progression in clinical cohorts.CPT: pharmacometrics & systems pharmacology · 2023Article
- A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development.Journal of pharmacokinetics and pharmacodynamics · 2023Review
- A population pharmacodynamic Markov mixed-effects model for determining remimazolam-induced sedation when co-administered with fentanyl in procedural sedation.Clinical and translational science · 2021Article
- Population Pharmacokinetics of Intracellular 5-Fluorouridine 5'-Triphosphate and its Relationship with Hand-and-Foot Syndrome in Patients Treated with Capecitabine.The AAPS journal · 2021Article
- Evaluation of patient-reported severity of hand-foot syndrome under capecitabine using a Markov modeling approach.Cancer chemotherapy and pharmacology · 2020Observational
- Analysis of Longitudinal-Ordered Categorical Data for Muscle Spasm Adverse Event of Vismodegib: Comparison Between Different Pharmacometric Models.CPT: pharmacometrics & systems pharmacology · 2020Article
- A Bounded Integer Model for Rating and Composite Scale Data.The AAPS journal · 2019Article
- A Novel Method for Analysing Frequent Observations from Questionnaires in Order to Model Patient-Reported Outcomes: Application to EXACT® Daily Diary Data from COPD Patients.The AAPS journal · 2019Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this work, an alternative model to discrete-time Markov model (DTMM) or standard continuous-time Markov model (CTMM) for analyzing ordered categorical data with Markov properties is presented: the minimal CTMM (mCTMM). Through a CTMM reparameterization and under the assumption that the transition rate between two consecutive states is independent on the state, the Markov property is expressed through a single parameter, the mean equilibration time, and the steady-state probabilities are described by a proportional odds (PO) model. The mCTMM performance was evaluated and compared to the PO model (ignoring Markov features) and to published Markov models using three real data examples: the four-state fatigue and hand-foot syndrome data in cancer patients initially described by DTMM and the 11-state Likert pain score data in diabetic patients previously analyzed with a count model including Markovian transition probability inflation. The mCTMM better described the data than the PO model, and adequately predicted the average number of transitions per patient and the maximum achieved scores in all examples. As expected, mCTMM could not describe the data as well as more flexible DTMM but required fewer estimated parameters. The mCTMM better fitted Likert data than the count model. The mCTMM enables to explore the effect of potential predictive factors such as drug exposure and covariates, on ordered categorical data, while accounting for Markov features, in cases where DTMM and/or standard CTMM is not applicable or conveniently implemented, e.g., non-uniform time intervals between observations or large number of categories.
Indexed as
Identifiers
What 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.