Sample sizes for brain atrophy outcomes in trials for secondary progressive multiple sclerosis.

Altmann, DR; Jasperse, B; Barkhof, F; Beckmann, K; Filippi, M; Kappos, LD; Molyneux, P; Polman, CH; Pozzilli, C; Thompson, AJ; Wagner, K; Yousry, TA; Miller, DH; (2008) Sample sizes for brain atrophy outcomes in trials for secondary progressive multiple sclerosis. Neurology, 72 (7). pp. 595-601. ISSN 0028-3878 DOI:

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BACKGROUND: Progressive brain atrophy in multiple sclerosis (MS) may reflect neuroaxonal and myelin loss and MRI measures of brain tissue loss are used as outcome measures in MS treatment trials. This study investigated sample sizes required to demonstrate reduction of brain atrophy using three outcome measures in a parallel group, placebo-controlled trial for secondary progressive MS (SPMS). METHODS: Data were taken from a cohort of 43 patients with SPMS who had been followed up with 6-monthly T1-weighted MRI for up to 3 years within the placebo arm of a therapeutic trial. Central cerebral volumes (CCVs) were measured using a semiautomated segmentation approach, and brain volume normalized for skull size (NBV) was measured using automated segmentation (SIENAX). Change in CCV and NBV was measured by subtraction of baseline from serial CCV and SIENAX images; in addition, percentage brain volume change relative to baseline was measured directly using a registration-based method (SIENA). Sample sizes for given treatment effects and power were calculated for standard analyses using parameters estimated from the sample. RESULTS: For a 2-year trial duration, minimum sample sizes per arm required to detect a 50% treatment effect at 80% power were 32 for SIENA, 69 for CCV, and 273 for SIENAX. Two-year minimum sample sizes were smaller than 1-year by 71% for SIENAX, 55% for CCV, and 44% for SIENA. CONCLUSION: SIENA and central cerebral volume are feasible outcome measures for inclusion in placebo-controlled trials in secondary progressive multiple sclerosis.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
PubMed ID: 19005170
Web of Science ID: 263386100005


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