The Role of Statistical Thinking in Biopharmaceutical Research

Abstract

The development of new drugs has evolved dramatically over the past decade. Advances in technology enable scientists to generate “big data” faster than ever before. The availability of complex, high-volume data in turn creates demand for innovative quantitative solutions and tools in a rapidly evolving landscape. As a result, the role of the statistical scientist in collaborative research has never been more important. Reflecting on these changes, Cox (2012) wrote, “…[A]lthough the tactics of statistical analysis have been utterly changed… the strategy of research design and analysis has been much less affected…” In this article, we argue that the practice of statistics is built on the foundation of good statistical thinking and consists of a complex combination of problem-solving skills, the essence of what Cox meant by the “strategy of research.” Although others have highlighted the role of statistical thinking in research design and analysis, in the age of data science, machine learning and artificial intelligence, it cannot be emphasized enough. We outline four general steps that contribute to good statistical thinking and illustrate them with five use cases (“vignettes”) as well as a detailed case study discussion from a maintenance therapy clinical trial for depression.