TY - JOUR
T1 - Unlocking Cognitive Analysis Potential in Alzheimer's Disease Clinical Trials
T2 - Investigating Hierarchical Linear Models for Analyzing Novel Measurement Burst Design Data
AU - Wang, Guoqiao
AU - Hassenstab, Jason
AU - Li, Yan
AU - Aschenbrenner, Andrew J.
AU - McDade, Eric M.
AU - Llibre-Guerra, Jorge
AU - Bateman, Randall J.
AU - Xiong, Chengjie
N1 - Publisher Copyright:
© 2024 John Wiley & Sons Ltd.
PY - 2024/12/30
Y1 - 2024/12/30
N2 - Measurement burst designs typically administer brief cognitive tests four times per day for 1 week, resulting in a maximum of 28 data points per week per test for every 6 months. In Alzheimer's disease clinical trials, utilizing measurement burst designs holds great promise for boosting statistical power by collecting huge amount of data. However, appropriate methods for analyzing these complex datasets are not well investigated. Furthermore, the large amount of burst design data also poses tremendous challenges for traditional computational procedures such as SAS mixed or Nlmixed. We propose to analyze burst design data using novel hierarchical linear mixed effects models or hierarchical mixed models for repeated measures. Through simulations and real-world data applications using the novel SAS procedure Hpmixed, we demonstrate these hierarchical models' efficiency over traditional models. Our sample simulation and analysis code can serve as a catalyst to facilitate the methodology development for burst design data.
AB - Measurement burst designs typically administer brief cognitive tests four times per day for 1 week, resulting in a maximum of 28 data points per week per test for every 6 months. In Alzheimer's disease clinical trials, utilizing measurement burst designs holds great promise for boosting statistical power by collecting huge amount of data. However, appropriate methods for analyzing these complex datasets are not well investigated. Furthermore, the large amount of burst design data also poses tremendous challenges for traditional computational procedures such as SAS mixed or Nlmixed. We propose to analyze burst design data using novel hierarchical linear mixed effects models or hierarchical mixed models for repeated measures. Through simulations and real-world data applications using the novel SAS procedure Hpmixed, we demonstrate these hierarchical models' efficiency over traditional models. Our sample simulation and analysis code can serve as a catalyst to facilitate the methodology development for burst design data.
KW - Alzheimer's disease
KW - SAS
KW - hierarchical linear mixed effects model
KW - hierarchical mixed models for repeated measures
KW - measurement burst design data
UR - http://www.scopus.com/inward/record.url?scp=85210096552&partnerID=8YFLogxK
U2 - 10.1002/sim.10292
DO - 10.1002/sim.10292
M3 - Article
C2 - 39586645
AN - SCOPUS:85210096552
SN - 0277-6715
VL - 43
SP - 5898
EP - 5910
JO - Statistics in medicine
JF - Statistics in medicine
IS - 30
ER -