Although biomedical technology has advanced, chronic diseases such as Alzheimer’s disease, autoimmune diseases, and diabetes remain difficult to treat. While researchers are continuing to search for effective treatment for these diseases, owing to the long duration and slow progression of chronic diseases, many challenges arise for the design and analysis of chronic disease clinical trials. For example, chronic disease clinical trials would require a long treatment period, leading to a high percentage of missing data and concerns about the validity of statistical analysis models; on the other hand, if the efficacy of a drug for chronic disease is only studied in relatively short-term clinical trials, could we or how would we extend the short-term findings to long term? What are clinically meaningful efficacy endpoints and what are appropriate statistical methods to analyze them? In this session, we will discuss the statistical issues in chronic disease clinical trials. Our first speaker is Professor Michael Donohue from the University of Southern California. He is also a member of the American Statistical Association Alzheimer’s Disease Scientific Working Group. He will discuss estimands and estimation methods for Alzheimer’s disease clinical trials. Our second speaker is Dr. Sue-Jane Wang from CDER/FDA. She will talk about statistical approaches to biomarker endpoints in the context of chronic disease clinical trials with examples. Dr. James Hung from CDER/FDA will be the discussant for this session.