Center Co-Director to give a talk on N-of-1 Trials at the University of Rochester Medical Center

October 5, 2017

Professor of Biostatistics and Center Co-Director, Chris Schmid, will give a talk at the URMC Department of Biostatistics and Computational Biology Fall Colloquia.

Schmid's talk is titled, "N-of-1 Trials for Making Personalized Treatment Decisions with Personalized Designs Using Self-Collected Data".  The talk will take place today, October 5 at 4pm at the school's Helen Wood Hall. 

Abstract: N-of-1 trials hold great promise for enabling participants to create personalized protocols to make personalized treatment decisions. Fundamentally, N-of-1 trials are single-participant multiple-crossover studies for determining the relative comparative effectiveness of two or more treatments for one individual. An individual selects treatments and outcomes of interest, carries out the trial, and then makes a final treatment decision with or without a clinician based on results of the trial. Established in a clinical environment, an N-of-1 practice provides data on multiple trials from different participants. Such data can be combined using meta-analytic techniques to inform both individual and population treatment effects. When participants undertake trials with different treatments, the data form a treatment network and suggest use of network meta-analysis methods. This talk will discuss ongoing and completed clinical research projects using N-of-1 trials for chronic pain, atrial fibrillation, inflammatory bowel disease, fibromyalgia and attention deficit hyperactivity disorder. Several of these trials collect data from participants using mobile devices. I will describe design, data collection and analytic challenges as well as unique aspects deriving from use of the N-of-1 design and mobile data collection for personalized decision-making. Challenges involve defining treatments, presenting results, assessing model assumptions and combining information from multiple participants to provide a better estimate of each individual’s effect than from his or her own data alone.