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Naihua Duan: Personalized biostatistics, small data, and N-of-1 trials

Personalized biostatistics, small data, and N-of-1 trials

Naihua Duan, Ph.D.
Columbia University

As personalized medicine is emerging as a promising paradigm to improve clinical decision-making, to customize clinical decisions for individual patients, to accommodate the unique needs and preferences for each specific patient, there is a growing need for biostatistical methods to be developed and deployed to serve the needs for this emerging paradigm. As an example, the on-going NINR-funded PREEMPT Study,, has developed a smartphone app that allows chronic pain patients and clinicians to run personalized experiments (n-of-1 trials), comparing two different pain treatments, to help patients and their clinicians to choose the most appropriate pain treatment for each individual patient. Such personalized biostatistical toolkits can be utilized by frontline clinicians and their patients to address the specific clinical questions confronted by each individual patient, to enable the specification and execution of the personalized trial protocol, to facilitate the collection of outcome and process data, to analyze and interpret the data acquired, and to produce reports to the end users to help them with evidence-based decision making. This paradigm exemplifies the potential for “Small Data” (as opposed to “Big Data”) to be deployed in clinical applications for the benefits of today’s patients as the primary aim for the evidence-based investigations. Importantly, personalized biostatistics based on Small Data provides strong incentives for end-users to participate in the evidence-based investigations, as they are targeted to benefit directly from such investigations, instead of the traditional clinical research that aims primarily to benefit future patients. There is a promising potential this merging paradigm will play an important role in the future of health and related domains, to supplement the traditional top-down model for research with a bottom-up model for quality improvement.