ArticleStatistics in medicine2017
Multidimensional latent trait linear mixed model: an application in clinical studies with multivariate longitudinal outcomes.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Longitudinal Evaluation of an Abbreviated Patient-Reported Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) for Predicting Dopaminergic Therapy Initiation in Early Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- Dynamic Prediction Using Functional Latent Trait Joint Models for Multivariate Longitudinal Outcomes: An Application to Parkinson's Disease.Statistics in medicine · 2025Article
- Application of longitudinal item response theory models to modeling Parkinson's disease progression.CPT: pharmacometrics & systems pharmacology · 2022Article
- Dissecting the Domains of Parkinson's Disease: Insights from Longitudinal Item Response Theory Modeling.Movement disorders : official journal of the Movement Disorder Society · 2022Article
- Novel Approach to Movement Disorder Society-Unified Parkinson's Disease Rating Scale Monitoring in Clinical Trials: Longitudinal Item Response Theory Models.Movement disorders clinical practice · 2021Article
- Longitudinal association between an overall diet quality index and latent profiles of cardiovascular risk factors: results from a population based 13-year follow up cohort study.Nutrition & metabolism · 2021Article
- A Bayesian approach for individual-level drug benefit-risk assessment.Statistics in medicine · 2019Article
- Assessing tilavonemab efficacy in early Alzheimer's disease via longitudinal item response theory modeling.Alzheimer's & dementia (New York, N. Y.)Article
- Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model.Statistical methods in medical researchArticle
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2 authors.
Funding
Abstract
Multilevel item response theory (MLIRT) models have been widely used to analyze the multivariate longitudinal data of mixed types (e.g., categorical and continuous) in clinical studies. The MLIRT models often have unidimensional assumption, that is, the multiple outcomes are clinical manifestations of a univariate latent variable. However, the unidimensional assumption may be unrealistic because some diseases may be heterogeneous and characterized by multiple impaired domains with variable clinical symptoms and disease progressions. We relax this assumption and propose a multidimensional latent trait linear mixed model (MLTLMM) to allow multiple latent variables and within-item multidimensionality (one outcome can be a manifestation of more than one latent variable). We conduct extensive simulation studies to assess the unidimensional MLIRT model and the proposed MLTLMM model. The simulation studies suggest that the MLTLMM model outperforms unidimensional model when the multivariate longitudinal outcomes are manifested by multiple latent variables. The proposed model is applied to two motivating studies of amyotrophic lateral sclerosis: a clinical trial of ceftriaxone and the Pooled Resource Open-Access ALS Clinical Trials database. Copyright © 2017 John Wiley & Sons, Ltd.
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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.