Use of band-pass filter analysis to evaluate outcomes in an antidepressant trial for treatment resistant patients

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 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.