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Novel non-linear models for clinical trial analysis with longitudinal data: A tutorial using SAS for both frequentist
Guoqiao Wang1,2, Whedy Wang3, Brian Mangal4
1Department of Neurology, School of Medicine, Washington University, St. Louis, Missouri, USA.
Statistics in Medicine
|May 10, 2024
Summary
This study introduces proportional models for analyzing longitudinal clinical trial data, offering a flexible alternative to traditional mixed models for repeated measures (MMRM) and linear mixed-effects models.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Longitudinal Data Analysis
Background:
- Longitudinal data in clinical trials are typically analyzed using mixed models for repeated measures (MMRM) or linear mixed-effects models.
- Inference often relies on absolute differences in adjusted mean change or rates of change.
- These methods can be limited in flexibility for complex data structures.
Purpose of the Study:
- To propose and demonstrate a novel proportional modeling approach for longitudinal clinical trial data.
- To offer a flexible method for analyzing disease progression relative to placebo decline.
- To showcase implementation using SAS procedures for both frequentist and Bayesian analyses.
Main Methods:
- Development of proportional models to estimate the percentage reduction in disease progression.
- Application of these models for simultaneous analysis of multiple cohorts, endpoints, and combined continuous/survival data.
- Implementation using SAS procedures, including the nlmixed procedure for MMRM analysis of response profiles.
Main Results:
- Simulated data confirmed the feasibility and flexibility of the proposed proportional models.
- The approach allows for innovative modeling of complex longitudinal data structures.
- A novel method for MMRM analysis using the nlmixed procedure was introduced.
Conclusions:
- Proportional models offer a flexible and innovative alternative for analyzing longitudinal clinical trial data.
- This approach enhances the ability to model disease progression and treatment effects.
- The demonstrated SAS implementations facilitate the adoption of these advanced statistical methods.
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