ArticleStatistics in medicine2024
Novel non-linear models for clinical trial analysis with longitudinal data: A tutorial using SAS for both frequentist and Bayesian methods.
Article in Statistics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Statistical models for Alzheimer's disease clinical trials: Lessons learned from the DIAN-TU Platform Trial.Journal of Alzheimer's disease : JAD · 2026Article
- Comments on "Novel Non-Linear Models for Clinical Trial Analysis With Longitudinal Data: A Tutorial Using SAS for Both Frequentist and Bayesian Methods".Statistics in medicine · 2025Article
- Letter to the Editor.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.PloS one · 2025Article
- Unlocking Cognitive Analysis Potential in Alzheimer's Disease Clinical Trials: Investigating Hierarchical Linear Models for Analyzing Novel Measurement Burst Design Data.Statistics in medicine · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed-effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure.
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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.