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F3‐02‐03: OPTIMALLY WEIGHTED ENDPOINTS FOR CLINICAL TRIALS IN MILD COGNITIVE IMPAIRMENT AND PRE‐CLINICAL ALZHEIMER'S DISEASE
Author(s) -
Edland Steven D.,
Ard Colin,
Raghavan Nandini
Publication year - 2014
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2014.04.254
Subject(s) - clinical dementia rating , clinical trial , rating scale , sample size determination , dementia , medicine , statistics , disease , mathematics , physical medicine and rehabilitation
estimates. Iteration continues until convergence. We extend this algorithm to include nonlinear effects on each outcome for age, gender, education,APOE4 genotype, A b, tau, and their interactions. Covariates are selected for each outcome at each stage of the iteration based on the Akaike Information Criterion using a generalized additive model approach. Outcome measures considered include assessments (ADAS13, MMSE, FAQ, and RAVLT), MRI brain volumetrics (hippocampus, ventricles, and entorhinal cortex), CSF (A b, tau, p-tau), and PET (PiB, Florbetapir, and FDG).Results: Similar attempts at this estimation have stratified by covariates or ignored them altogether. Our current approach will allow closed form, as opposed to bootstrap, estimation of important covariate effects for each outcome. Covariate adjustment for age is particular important since our progression curve estimates span 20 years. Conclusions: The proposed method provides improved estimates of long-term progression curves, interrogation of covariate effects, and inspection of different patterns of progression.