Use of band-pass filter analysis to evaluate Outcomes In an Antidepressant Trial for Treatment Resistant Patients

European Neuropsychopharmacology, June 2014

Steven D. Targum, Daniel J. Burch, Mahnaz Asgharnejad, Timothy Petersen, Roberto Gomeni, Maurizio Favae

Abstract
Band-pass filtering is a novel statistical methodology that proposes that filtering out data from trial sites generating non-plausible high or low levels of placebo response can yield a more accurate effect size and greater separation of active drug (when efficacious) from placebo.

We applied band-pass filters to re-analyze data from a negative antidepressant trial (NCT00739908) evaluating CX157 (a reversible and selective monoamine oxidase inhibitor-A) versus placebo.

360 patients from 29 trial sites were randomized to either CX157 treatment (n=182) or placebo (n=178). We applied two filters of<3 or>7 points (filter #1) or<3 and>9 points (filter #2) mean change of the total MADRS placebo scores for each site. Trial sites that had mean placebo MADRS score changes exceeding the boundaries of these band-pass filter thresholds were considered non-informative and all of the data from these sites were excluded from the post-hoc re-analysis.

The two band-pass filters reduced the sample of informative patients from 353 patients in the mITT population to 62 in filter #1 and 152 in the filter #2 group. The placebo response was reduced from 31.1% in the mITT population to 9.4% with filter #1 and 20.8% with filter #2. MMRM analysis revealed a non-statistically significant trend of p= 0.13 and 0.16 respectively for the two filters in contrast to the mITT population (p= 0.58).
Our findings support the band-pass filter hypothesis and highlight issues related to site-based scoring variability and inappropriate subject selection that may contribute to trial failure.