S3‐02‐01: Quantifying the placebo effect through modeling and simulation

not available. S3-01-02 IS THERE A NEED FOR LARGER PREVENTION TRIALS? Laura Fratiglioni, Aging Research Center, Karolinska Institutet & Stockholm University, Stockholm, Sweden. Contact e-mail: laura. fratiglioni@ki.se Abstract not available. S3-01-03 EXPERIENTIAL AND PSYCHOSOCIAL RISKnot available. S3-01-03 EXPERIENTIAL AND PSYCHOSOCIAL RISK FACTORS FOR DEMENTIA David A. Bennett, Rush University Medical Center, Chicago, IL, USA. Contact e-mail: David_A_Bennett@rush.edu Abstract not available.not available. S3-01-04 IS IT TIME TO GO BEYOND RANDOMIZED CONTROLLED TRIALS? Kaarin J. Anstey, Centre for Research on Ageing, Health and Wellbeing, Dementia Collaborative Research Centre, Research School of Population Health, The Australian National University, Canberra, Australia. Contact e-mail: kaarin.anstey@anu.edu.au Abstract not available.not available. TUESDAY, JULY 21, 2015 SYMPOSIA S3-02 NOVELTRIAL DESIGN S3-02-01 QUANTIFYING THE PLACEBO EFFECT THROUGH MODELING AND SIMULATION Klaus Romero, Kaori Ito, Brian Corrigan, Richard J. Anziano, Jon Neville, Diane Stephenson, Richard Lalonde, CAMD AD Modeling and Simulation Team, Coalition Against Major Diseases, Critical Path Institute, Tucson, AZ, USA; Pfizer, Groton, CT, USA; Critical Path Institute, Tucson, AZ, USA. Contact e-mail: kromero@c-path.org Background:Understanding the magnitude, duration and variability of the placebo effect in longitudinal Alzheimer’s disease (AD) studies is critical in order to optimally design clinical trials and interpret their results. Standardized and integrated data, along with literature data, provide a platform to develop quantitative models for the placebo effect. Methods:The placebo response component from an FDA/EMA-endorsed drug-disease-trial model for AD, developed from ADNI, CAMD and the literature, is used to illustrate disease progression in the control groups from various case studies. Results: The case studies show how the varying placebo response in control arms has an impact in understanding the magnitude of drug effect in clinical trials. Relevant covariates like baseline disease severity are important factors to take into account when comparing outcomes between groups. Study duration, sample size and study design are important factors to consider when interpreting a trial’s results. These case studies show that failures in late-stage studies are not likely due to insufficient cognitive decline in the control groups. Conclusions: Meta-analytic approaches that integrate all available relevant data provide a quantitative understanding of placebo effect, disease progression and potential interpretation of treatment effects, which offers a useful tool to optimize trial design and interpretation. S3-02-02 PREVENTION TRIALS CraigW. Ritchie, EPAD Consortium,University of Edinburgh, Edinburgh, United Kingdom. Contact e-mail: craig.ritchie@ed.ac.uk Background:An ever-increasing understanding of neurodegenerative disease processes preceding dementia coupled to a lack of success in developing disease modifying drugs for this condition have driven a need to undertake secondary prevention trials differently. To address this problem, a significant public-private partnership was developed by the European IMI (Innovative Medicines Initiative). Through this award, the EPAD (European Prevention of Alzheimer’s Dementia) Project was established. Methods:EPAD will draw together 24,000 subjects from existing cohorts and registers in Europe to then enter