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Use of white matter reference regions for detection of change in florbetapir positron emission tomography from completed phase 3 solanezumab trials
Author(s) -
Fleisher Adam S.,
Joshi Abhinay D.,
Sundell Karen L.,
Chen YunFei,
KollackWalker Sara,
Lu Ming,
Chen Sherry,
Devous Michael D.,
Seibyl John,
Marek Kenneth,
Siemers Eric R.,
Mintun Mark A.
Publication year - 2017
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.2017.02.009
Subject(s) - positron emission tomography , nuclear medicine , sample size determination , normalization (sociology) , placebo , medicine , mathematics , statistics , pathology , alternative medicine , sociology , anthropology
We compared subject‐specific white matter (SSWM) and whole cerebellum (CBL) reference regions for power to detect longitudinal change in amyloid positron emission tomography signal. Methods Positive florbetapir positron emission tomography scans were analyzed from participants (66 placebo treated and 63 solanezumab treated) with mild dementia caused by Alzheimer's disease from the EXPEDITION and EXPEDITION2 studies. For comparison to CBL, a second normalization was performed on longitudinal data using an SSWM correction factor (SSWM normalization ratio [SSWMnr]). Analysis of covariance assessed baseline to 18‐month change between treatment with solanezumab and placebo. Sample and effect size estimations provided magnitude of observed treatment changes. Results Longitudinal percent change between placebo and solanezumab using CBL was not significant ( P = .536) but was significant for SSWMnr ( P = .042). Compared with CBL, SSWMnr technique increased the power to detect a treatment difference, more than tripling the effect size and reducing the sample size requirements by 85% to 90%. Discussion Adjusting longitudinal standardized uptake value ratios with an SSWM reference region in these antiamyloid treatment trials increased mean change detection and decreased variance resulting in the substantial improvement in statistical power to detect change.