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Division of Biostatistics and Bioinformatics

Faculty of the division have research interests in design and analysis of clinical trials, observational studies, and biomarker studies, including such areas as longitudinal models, lifetime data analysis, “big” and high dimensional data analysis, image analysis, causal inference and survey statistics.

Education & Training

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Ronghui (Lily) Xu, PhD, Professor - Division Lead


Charles Berry, PhD, Professor
Steven Edland, PhD, Professor
Sonia Jain, PhD, MSc, Professor and Associate Dean for Research
Lin Liu, PhD, Associate Professor
Karen Messer, PhD, Professor
Loki Natarajan, PhD, Professor
Armin Schwartzman, PhD, Professor
Ronald Thomas, PhD, Professor 
Xin Tu, PhD, Professor
Florin Vaida, PhD, Professor 
Xinlian Zhang, PhD, Assistant Professor
Jingjing Zou, PhD, Assistant Professor


Upcoming Seminars

The Design and Analysis of Experiments Under Interference Using Partial Network Data

Oct. 4, 2023, 1 pm

Tyler Mccormick, PhD, Professor, Department of Biostatistics, University of Washington
Abstract: The stable unit treatment value assumption states that the outcome of an individual is not affected by the treatment statuses of the individual's neighbors. In many common scenarios, ranging from economics to epidemiology, this assumption is not met. For instance, an individual's likelihood of being infected once given a vaccine likely depends on whether their close contacts received the vaccine. In many empirically relevant situations, full network data (required to adjust for these spillover effects) is too costly or logistically infeasible to collect. Partially or indirectly observed network data (e.g. subsamples, aggregated relational data (ARD), egocentric sampling, or respondent-driven sampling) reduce the logistical and financial burden of collecting network data, but the statistical properties of treatment effect adjustments from these design strategies were, until now, largely unknown. In this paper, we present a framework for consistent estimation of treatment effect adjustments using certain types of partial network data. We present simple, widely applicable, sufficient conditions on the sampling design that ensure consistent estimation of the treatment effects.  Further, we demonstrate how to use partial network data to inform randomization in experimental settings to reduce the variance of the treatment effect estimate. In addition to our theoretical results, we evaluate this approach using simulated experiments on observed graphs.