TITLE: N-of-1 Trials for Personalized Healthcare
SPEAKER: Christopher Schmid, Brown University and Naihua Duan, Columbia University
MODERATOR: Alfred H. Balch


Personalized (N-of-1) trials hold great promise for broadening the clinical knowledge production enterprise to engage individuals in trial design, creation and use of personal data, and decision making. N-of-1 trials use a multi-crossover design in which each individual receives two or more treatments multiple times in a randomized order. In contrast to traditional clinical trial designs, N-of-1 designs can measure individual treatment efficacy to create personalized knowledge. By combining individual trials in a multilevel structure, they can also assess average treatment effects in populations and subgroups and measure treatment effect heterogeneity to create generalizable knowledge. N-of-1 trials may be deployed in a variety of ways. Individuals may create unique, personal designs focused on treatments and outcomes of interest carried out in a manner best suited to them. Or trials may be coordinated to have similar protocols facilitating the sharing and combining of information to learn about groups of individuals as well. Such designs may better inform individuals too through borrowing of strength from the findings of exchangeable group members. Such group designs may be particularly valuable in clinical settings such as healthcare organizations which provide personalized care to groups of individuals. We discuss the promise and challenges of N-of-1 trials, including the use of software to design and analyze trials, the use of mobile apps to facilitate participation, retain interest, collect data and provide interpreted results to participants, and some of the research barriers that need to be overcome, particularly the challenges of accommodating personalized protocols. These issues are illustrated by several of our recent projects each involving many N-of-1 trials in which we combined mobile device applications with server-driven statistical analytics using an R package to return results to individuals. We discuss defining treatments and sequences of treatments, synthesizing treatment networks, incorporating patient-specific prior information, automating the choice of appropriate statistical models and assessment of model assumptions, and automating graphical displays and text to facilitate appropriate interpretation by non-technical users.

Instructors’ Biography:

Dr. Christopher Schmid is Professor of Biostatistics at Brown University School of Public Health where he co-founded the Center for Evidence Synthesis in Health. He directs the Biostatistics, Epidemiology and Research Design (BERD) Core of the Rhode Island Center to Advance Translational Science. He is a Fellow of the American Statistical Association, founding Editor of the journal Research Synthesis Methods, long-time statistical editor of the American Journal of Kidney Diseases and former member of the Drug Safety and Risk Management Committee for FDA. His research focuses on Bayesian methods for meta-analysis, methods for developing and assessing predictive models using data from multiple sources and on methods for design and analysis of N-of-1 trials. He has a long record of collaborative research in diverse areas of medicine and health with academia, government and industry and nearly 300 peer-reviewed publications. He has coauthored consensus CONSORT reporting guidelines for N-of-1 trials and single-case designs, and PRISMA guidelines extensions for meta-analysis of individual participant studies and for network meta-analyses as well as the Institute of Medicine report that established US standards for systematic reviews. He is lead statistician on several N-of-1 trial consortia. Dr. Schmid graduated from Haverford College with a BA in Mathematics and received his PhD in Statistics from Harvard University.


Dr. Naihua Duan is an accomplished practicing biostatistician with research interests in a variety of domains, including implementation research, quality improvement investigations, health services research, prevention research, sample design and experimental design, model robustness, transformation models, multilevel modeling, nonparametric and semi-parametric regression methods, environmental exposure assessment, etc. He has published more than 200 papers in leading journals in statistics, medicine, and public health. He is an Elected Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics, and the co-founding chair (with Robert Gibbons) for the Mental Health Statistics Section of the ASA. He received the Long-Term Excellence Award from the Health Policy Statistics Section of the ASA in 2013, and the Annual Harvard Award in Psychiatric Epidemiology and Biostatistics in 2016. He received a B.S. in Mathematics from National Taiwan University, an M.A. in Mathematical Statistics from Columbia University, and a Ph.D. in Statistics from Stanford University. He worked at RAND from 1979 to 2000, advancing from Associate Statistician to Corporate Chair and Senior Fellow in Statistics. From 2000 to 2007, he served as Professor in Residence, with tenure, in the Departments of Biostatistics and Psychiatry at UCLA. From 2007 to 2012, he served as Professor of Biostatistics (in Psychiatry), with tenure, in the Departments of Biostatistics and Psychiatry; Research Scientist in the New York State Psychiatric Institute; and Director for the Division of Biostatistics in the Department of Psychiatry at Columbia University and New York State Psychiatric Institute. He retired in 2012 from Columbia University and New York State Psychiatric Institute, and continued to conduct research and consulting at a leisurely pace, while enjoying his golden years with his wife ChihMing Fan and their grandchildren, Alex, Evelina, and Theodore.

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