A Novel Linear B‐Spline Mixed Effect Model for Alzheimer’s Disease Clinical Study Data

The LB-MMRM is a natural extension of traditional MMRM for regulatory review and clinical application. Its ability to incorporate unscheduled visits, integrate studies with varying visit schedules, and provide intuitive and straightforward implementation makes it a promising alternative to existing models. This approach addresses key limitations in longitudinal clinical trial analyses.

Authors

Institutions

Publication Details

Journal
Alzheimer s & Dementia
Published
2025-12-01
DOI
https://doi.org/10.1002/alz70859_099565
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Novel Linear B‐Spline Mixed Effect Model for Alzheimer’s Disease Clinical Study Data

Kun Jin, Guoqiao Wang
Alzheimer s & Dementia
Statistical Methods and Bayesian Inference
article

A Novel Linear B‐Spline Mixed Effect Model for Alzheimer’s Disease Clinical Study Data

Kun Jin, Guoqiao Wang
article en

Abstract

The LB-MMRM is a natural extension of traditional MMRM for regulatory review and clinical application. Its ability to incorporate unscheduled visits, integrate studies with varying visit schedules, and provide intuitive and straightforward implementation makes it a promising alternative to existing models. This approach addresses key limitations in longitudinal clinical trial analyses.

Alzheimer s & DementiaVol. 21(S5)
Washington University in St. Louis (US), Enzo Life Sciences (United States) (US)
Openalex Percentile: Top 23%
Statistical Methods and Bayesian Inference
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.