Premium
P1‐040: Clinical Dementia Rating‐Sum of Boxes Responder Analysis in Prime: A Randomized Phase 1B Study of the Anti‐Amyloid Beta Monoclonal Antibody Aducanumab (BIIB037)
Author(s) -
Viglietta Vissia,
O'Gorman John,
Williams Leslie,
Chen Tianle,
Chiao Ping,
Boot Brendon,
Hock Christoph,
Nitsch Roger M.,
Sandrock Alfred
Publication year - 2016
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.2016.06.787
Subject(s) - clinical dementia rating , tolerability , medicine , clinical endpoint , population , placebo , post hoc analysis , clinical trial , surrogate endpoint , dementia , oncology , adverse effect , disease , pathology , alternative medicine , environmental health
logical and age-related disorders and 3) report and comment on the clinical utility of the biomarker signature. In this analysis, we expand on Lunnon et al. 2012 and address all of these concerns. Methods:We obtained sixteen gene expression data sets, including two additional AD data sets, neurological, autoimmune, diabetes and arterial related disorders from public and in-house sources. All predictive modelling was performed using the R package ‘caret’ with 1000 bootstrap resamples used to evaluate model performance. We re-developed the Random Forest (RF) AD classification model published in Lunnon et al. 2012, using 79 AD-cases and 79 controls for training.We evaluated themodel in two independent AD datasets, generated using two different microarray platforms (Illumina N1⁄4221 and Affymetrix N1⁄439). To assess specificity for AD, the two AD expression sets and all Non-AD data were combined and used as a large test/validation dataset totalling 1839 samples, on which, the AD RF classifier was tested for its ability to predict AD from non-AD. Results: The AD RF classification model achieved a training accuracy of 88%, and a mean test accuracy of 68.88% (95% CI, 62.74-74.51%) within the first independent AD dataset (Illumina) and 59.52%(95% CI, 45.79-74.34%) in the second independent AD dataset (Affymetrix). When evaluating the AD classification model’s ability to discriminate AD samples from non-AD samples, the model achieved a mean accuracy of 59.7% (95% CI, 55.13-64.08) and equated to a PPV of 0.6, NPV of 0.4, positive Clinical Utility Index of 0.39 and a negative Clinical Utility Index of 0.22. Conclusions: In its current form, the AD classification model’s clinical utility is ‘poor’ in AD discovery (confirmation) and a ’poor’ predictor for screening (ruling out) AD. Further investigations and comparisons of signatures in larger AD datasets and other disorders are required.