Background: Understanding disease progression, drug and placebo effects, as well as drop-outs in 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 model development and regulatory review processes are illustrated for an FDA/EMA-endorsed drug-disease-trial model for AD, developed from ADNI, CAMD and the literature, in order to showcase relevant aspects of communications between disciplines including pharmacometrics, statistics and neurology. 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.