ArticleJournal of pharmacokinetics and pharmacodynamics2018
Modeling near-continuous clinical endpoint as categorical: application to longitudinal exposure-response modeling of Mayo scores for golimumab in patients with ulcerative colitis.
Article in Journal of pharmacokinetics and pharmacodynamics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- Application of Beta-Distribution and Combined Uniform and Binomial Methods in Longitudinal Modeling of Bounded Outcome Score Data.The AAPS journal · 2020Trial
- Likelihood comparisons in bounded outcome score analysis must be internally consistent.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Latent variable indirect response modeling of clinical efficacy endpoints with combination therapy: application to guselkumab and golimumab in patients with ulcerative colitis.Journal of pharmacokinetics and pharmacodynamics · 2023Article
- Role of Disease Progression Models in Drug Development.Pharmaceutical research · 2022Review
- A longitudinal model for the Mayo Clinical Score and its sub-components in patients with ulcerative colitis.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Comparison of Precision and Accuracy of Five Methods to Analyse Total Score Data.The AAPS journal · 2020Article
- Facilitating Longitudinal Exposure-Response Modeling of a Composite Endpoint Using the Joint Modeling of Sparsely and Frequently Collected Subcomponents.The AAPS journal · 2020Article
- Applying Beta Distribution in Analyzing Bounded Outcome Score Data.The AAPS journal · 2020Article
- On the Comparison of Methods in Analyzing Bounded Outcome Score Data.The AAPS journal · 2019Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate characterization of exposure-response relationship of clinical endpoints is important in drug development to identify optimal dose regimens. Endpoints with ≥ 10 ordered categories are typically analyzed as continuous. This manuscript aims to show circumstances where it is advantageous to analyze such data as ordered categorical. The results of continuous and categorical analyses are compared in a latent-variable based Indirect Response modeling framework for the longitudinal modeling of Mayo scores, ranging from 0 to 12, which is commonly used as a composite endpoint to measure the severity of ulcerative colitis (UC). Exposure response modeling of Mayo scores is complicated by the fact that studies typically include induction and maintenance phases with re-randomizations and other response-driven dose adjustments. The challenges are illustrated in this work by analyzing data collected from 3 phase II/III trials of golimumab in patients with moderate-to-severe UC. Visual predictive check was used for model evaluations. The ordered categorical approach is shown to be accurate and robust compared to the continuous approach. In addition, a disease progression model with an empirical bi-phasic rate of onset was found to be superior to the commonly used placebo model with one onset rate. An application of this modeling approach in guiding potential dose-adjustment was illustrated.
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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.