Seminar Series

The Applied Biostatistics Seminar Series consists of a series of talks with the primary purpose of furthering statistical knowledge on an applied level. Talks will focus on advances in biostatistical methods and statistical programming techniques and their translation into addressing biomedical research questions. The seminars are open to all members of the Duke community, but primarily geared toward applied statistical researchers.

Personalized Policy Learning using Longitudinal Mobile Health Data

Min Qian, PhD
Friday, October 16, 2020 - 12:00 at Zoom

Abstract: Personalized policy represents a paradigm shift from one decision rule for all users to an individualized decision rule for each user. Developing personalized policy in mobile health applications imposes challenges. First, for lack of adherence, data from each user are limited. Second, unmeasured contextual factors can potentially impact on...
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Propensity score weighting under limited overlap and model misspecification

Roland Matsouaka, PhD
Friday, September 25, 2020 - 10:00 at Zoom Conference

Abstract: Propensity score (PS) weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting (IPW), assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates...
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Personalized Dynamic Treatment Regimes in Continuous Time: A Bayesian Joint Model for Optimizing Clinical Decisions with Timing

Yanxun Xu, PhD
Friday, September 11, 2020 - 09:00 at Zoom

Abstract: Accurate models of clinical actions and their impacts on disease progression are critical for estimating personalized optimal dynamic treatment regimes (DTRs) in medical/health research, especially in managing chronic conditions. Traditional statistical methods for DTRs usually focus on estimating the optimal treatment or dosage at each given medical intervention, but...
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Sharp Inference on Selected Subgroups in Observational Studies

Jingshen Wang, PhD
Friday, August 28, 2020 - 01:30 at Zoom

Abstract: In the study of high-dimensional observational data (e.g., program evaluation, electronic health records data, health care claim database, and educational research), making inference on the maximum treatment effect is particularly helpful for answering some important questions in subgroup analysis, where selecting and making inference on treatment effect for the...
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Imputation and Causal Inference in Genomics

Audrey Qiuyan Fu, PhD
Thursday, August 13, 2020 - 12:00 at Zoom

Abstract: Genomic data can be complex, large, noisy and sparse. Here I will discuss two problems we have worked on. The first problem deals with the highly sparse data from experiments of measuring gene expression in single cells. These data contain a large number of zeros (often >80%); many of...
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