Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
This week's weekly Biostatistics Seminar will feature Dr. Donglin Zeng, professor at UNC Gillings School of Global Public Health, and the co-director of the Carolina Survey Research Laboratory
Sequential Multiple Assignment Randomization Trials with Enrichment Design
Sequential multiple assignment randomization trial (SMART) is a powerful design to study Dynamic Treatment Regimes (DTRs) and allows causal comparisons of DTRs. To handle practical challenges of SMART, we propose a SMART with Enrichment design (SMARTer), which performs stage-wise enrichment for SMART. Specifically, at each subsequent stage of a SMART, we enrich the study sample with new patients who have received previous stages' treatments in a naturalistic fashion without randomization, and only randomize them among the current stage treatment options. One extreme case of the SMARTer is to synthesize separate independent single-stage randomized trials with patients who have received previous stage treatments. We show data from SMARTer allows for unbiased estimation of DTRs as SMART does under certain assumptions. Furthermore, we show analytically that the efficiency gain of the new design over SMART can be significant especially when the dropout rate is high. Lastly, extensive simulation studies are performed to demonstrate performance of SMARTer design, and sample size estimation in a scenario informed by real data from a SMART study is presented.
*This is joint work with Drs. Ying Liu and Yuanjia Wang
Last Updated On Tuesday, August 30, 2016 by Valenti, Renee Nerozzi
Created On Thursday, July 14, 2016