TY - JOUR
T1 - Novel non-linear models for clinical trial analysis with longitudinal data
T2 - A tutorial using SAS for both frequentist and Bayesian methods
AU - Wang, Guoqiao
AU - Wang, Whedy
AU - Mangal, Brian
AU - Liao, Yijie
AU - Schneider, Lon
AU - Li, Yan
AU - Xiong, Chengjie
AU - McDade, Eric
AU - Kennedy, Richard
AU - Bateman, Randall
AU - Cutter, Gary
N1 - Publisher Copyright:
© 2024 John Wiley & Sons Ltd.
PY - 2024/7/10
Y1 - 2024/7/10
N2 - 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.
AB - 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.
KW - Bayesian multivariate model
KW - SAS
KW - mixed models for repeated measures
KW - multivariate endpoint
KW - proportional joint model
UR - http://www.scopus.com/inward/record.url?scp=85192745744&partnerID=8YFLogxK
U2 - 10.1002/sim.10089
DO - 10.1002/sim.10089
M3 - Article
C2 - 38727205
AN - SCOPUS:85192745744
SN - 0277-6715
VL - 43
SP - 2987
EP - 3004
JO - Statistics in medicine
JF - Statistics in medicine
IS - 15
ER -