The last 50 years (and especially the last 10-15) have seen a burgeoning in collaborations among academic, industry, and government statisticians. Industry statisticians have faced increasing pressure to develop and utilize more efficient statistical techniques
while simultaneously coping with upper management requirements to control costs. Meanwhile, academics too have faced increasing pressure from deans and department heads to obtain external funding support for their research at the same time that the budgets
of their primary funding agencies (NIH, NSF, DoD, AHRQ, etc.) are flat or shrinking. Fortunately, many problems of mutual research interest have created a modest "win-win" in this difficult environment, with academics supervising industry-supported PhD students
doing statistical methods development and programming more cheaply and efficiently that the companies can do it themselves, and while providing the students with cutting-edge dissertation topics. In this talk I review several examples of successful collaborations
from my own work in the design and analysis of Bayesian adaptive clinical trials, including (time permitting) applications in medical device trial design, adaptive borrowing of strength from historical data, network meta-analysis to assess a drug's competitive
position, novel methods for rare and pediatric diseases, and Bayesian approaches for subgroup identification. I also describe efforts to obtain buy-in from government regulators, a key step in this process. While agreements over publication and intellectual
property (IP) rights are a constant challenge, on the whole the collaborative model I discuss has worked well, and offers a blueprint for future expansion.