ArticleThe AAPS journal2020
Applying Beta Distribution in Analyzing Bounded Outcome Score Data.
Article in The AAPS journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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.
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Who cites it
6 citing papers in PubMed.
- Employing zero-inflated beta distribution in an exposure-response analysis of TYK2/JAK1 inhibitor brepocitinib in patients with plaque psoriasis.Journal of pharmacokinetics and pharmacodynamics · 2024Trial
- Application of Beta-Distribution and Combined Uniform and Binomial Methods in Longitudinal Modeling of Bounded Outcome Score Data.The AAPS journal · 2020Trial
- Tutorial on Analyzing Patient-Reported Outcome Measures in Clinical Development.Clinical and translational science · 2026Review
- 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
- Improving categorical endpoint longitudinal exposure-response modeling through the joint modeling with a related endpoint.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Facilitating Longitudinal Exposure-Response Modeling of a Composite Endpoint Using the Joint Modeling of Sparsely and Frequently Collected Subcomponents.The AAPS journal · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disease status is often measured with bounded outcome scores (BOS) which report a discrete set of values on a finite range. The distribution of such data is often non-standard, such as J- or U-shaped, for which standard analysis methods assuming normal distribution become inappropriate. Most BOS analysis methods aim to either predict the data within its natural range or accommodate data skewness, but not both. In addition, a frequent modeling objective is to predict clinical response of treatment using derived disease endpoints, defined as meeting certain criteria of improvement from baseline in disease status. This objective has not yet been addressed in existing BOS data analyses. This manuscript compares a recently proposed beta distribution-based approach with the standard continuous analysis approach, using an established mechanism-based longitudinal exposure-response model to analyze data from two phase 3 clinical studies in psoriatic patients. The beta distribution-based approach is shown to be superior in describing the BOS data and in predicting the derived endpoints, along with predicting the response time course of a highly sensitive subpopulation.
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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.