Historically, advances in the statistical design of clinical trials have accompanied progress within the science and practice of computation. The early 1990s witnessed increased exploration of adaptive and group sequential methods, in no small part due to the enhanced calculations made possible by software that had been developed a decade prior. The similar expanse of designs and methods throughout the past two decades, and the novel departures from the traditional two-arm design, have come with the ability to quickly compute more intricate and complex algorithms. By the beginning of the 2010s, the alignment of biostatistics and computation had grown close enough for educators and academics to begin advocating that biostatisticians needed to be well-grounded in computational reasoning, to equip themselves for unchartered terrains of drug discovery.
Recent advances in computational tools have made the construction of high-efficiency clinical trials more rigorous than ever, with the ability to thoroughly explore a design space consisting of hundreds of thousands of possible simulations. Not only does such capability enable the optimization and de-risking of clinical trial design, it enables sponsors to ask questions they might never have had opportunity to explore before now.