Trial reportJournal of pharmacokinetics and pharmacodynamics2013
Latent variable indirect response modeling of categorical endpoints representing change from baseline.
Trial report in Journal of pharmacokinetics and pharmacodynamics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Population pharmacokinetics and exposure-response modeling analyses of guselkumab in patients with psoriatic arthritis.Clinical and translational science · 2022Trial
- Application of Beta-Distribution and Combined Uniform and Binomial Methods in Longitudinal Modeling of Bounded Outcome Score Data.The AAPS journal · 2020Trial
- Joint longitudinal model development: application to exposure-response modeling of ACR and DAS scores in rheumatoid arthritis patients treated with sirukumab.Journal of pharmacokinetics and pharmacodynamics · 2018Trial
- Improvement in latent variable indirect response modeling of multiple categorical clinical endpoints: application to modeling of guselkumab treatment effects in psoriatic patients.Journal of pharmacokinetics and pharmacodynamics · 2017Trial
- Information contributed by meta-analysis in exposure-response modeling: application to phase 2 dose selection of guselkumab in patients with moderate-to-severe psoriasis.Journal of pharmacokinetics and pharmacodynamics · 2014Trial
- Moving Beyond Boundaries: Utilization of Longitudinal Exposure-Response Model for Bounded Outcome Score to Inform Decision Making in the Accelerated Drug Development Paradigm.Clinical pharmacokinetics · 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
- Disease trajectory of SLE clinical endpoints and covariates affecting disease severity and probability of response: Analysis of pooled patient-level placebo (Standard-of-Care) data to enable model-informed drug development.CPT: pharmacometrics & systems pharmacology · 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
- Applying Beta Distribution in Analyzing Bounded Outcome Score Data.The AAPS journal · 2020Article
- Modeling near-continuous clinical endpoint as categorical: application to longitudinal exposure-response modeling of Mayo scores for golimumab in patients with ulcerative colitis.Journal of pharmacokinetics and pharmacodynamics · 2018Article
- A comprehensive evaluation of exposure-response relationships in clinical trials: application to support guselkumab dose selection for patients with psoriasis.Journal of pharmacokinetics and pharmacodynamics · 2018Article
- Landmark and longitudinal exposure-response analyses in drug development.Journal of pharmacokinetics and pharmacodynamics · 2017Article
- Evaluation of estimation, prediction and inference for autocorrelated latent variable modeling of binary data-a simulation study.Journal of pharmacokinetics and pharmacodynamics · 2016Article
- Improvement in latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint in rheumatoid arthritis.Journal of pharmacokinetics and pharmacodynamics · 2016Article
- Simultaneous Exposure-Response Modeling of ACR20, ACR50, and ACR70 Improvement Scores in Rheumatoid Arthritis Patients Treated With Certolizumab Pegol.CPT: pharmacometrics & systems pharmacology · 2014Article
- Latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint.Journal of pharmacokinetics and pharmacodynamics · 2014Article
- Exposure-response modeling of clinical end points using latent variable indirect response models.CPT: pharmacometrics & systems pharmacology · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate exposure-response modeling is important in drug development. Methods are still evolving in the use of mechanistic, e.g., indirect response (IDR) models to relate discrete endpoints, mostly of the ordered categorical form, to placebo/co-medication effect and drug exposure. When the discrete endpoint is derived using change-from-baseline measurements, a mechanistic exposure-response modeling approach requires adjustment to maintain appropriate interpretation. This manuscript describes a new modeling method that integrates a latent-variable representation of IDR models with standard logistic regression. The new method also extends to general link functions that cover probit regression or continuous clinical endpoint modeling. Compared to an earlier latent variable approach that constrained the baseline probability of response to be 0, placebo effect parameters in the new model formulation are more readily interpretable and can be separately estimated from placebo data, thus allowing convenient and robust model estimation. A general inherent connection of some latent variable representations with baseline-normalized standard IDR models is derived. For describing clinical response endpoints, Type I and Type III IDR models are shown to be equivalent, therefore there are only three identifiable IDR models. This approach was applied to data from two phase III clinical trials of intravenously administered golimumab for the treatment of rheumatoid arthritis, where 20, 50, and 70% improvement in the American College of Rheumatology disease severity criteria were used as efficacy endpoints. Likelihood profiling and visual predictive checks showed reasonable parameter estimation precision and model performance.
Indexed as
Identifiers
23275019What 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.