Current Bio3 Seminar Series

The Bio3 Seminar Series meets every second and fourth Friday of the month, during the academic year. Refreshments are served 15 minutes prior to the seminar's starting time.

*Students are expected to attend the bi-weekly seminar series*

SEMINAR SCHEDULE - SPRING 2019:

Friday, January 11, 2019 at 10:00 AM

Faming Liang, Ph.D.
Professor, Department of Statistics, Purdue University

Title: Extended Stochastic Gradient MCMC Algorithms for Large-Scale Bayesian Computing

Abstract: The stochastic gradient Markov chain Monte Carlo (SGMCMC) algorithms, such as stochastic gradient Langevin dynamics and stochastic gradient Hamilton Monte Carlo, have recently received much attention in Bayesian computing for large-scale data for which the sample size can be very large, or the dimension can be very high, or both. However, these algorithms can only be applied to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. We propose a class of extended SGMCMC algorithms which, by introducing appropriate latent variables and utilizing Fisher's identity, can be applied to more general large-scale Bayesian computing problems, such as those involving dimension jumping and missing data. For a large-scale dataset with sample size N and dimension p, the proposed algorithms can achieve a computational complexity of O(N^{1+\epsilon} p^{1-\epsilon'}) for some small constants \epsilon and \epsilon', which is quite comparable with the computational complexity O(N p^{1-\epsilon'}) achieved in general by the stochastic gradient descent (SGD) algorithm. The proposed algorithms are illustrated using high-dimensional variable selection, sparse deep learning with large-scale data, and a large-scale missing data problem. The numerical results show that the proposed algorithms have a significant computational advantage over traditional MCMC algorithms and can be highly scalable when mini-batch samples are used in simulations. Compared to frequentist methods, they can produce more accurate variable selection and prediction results, while exhibiting similar CPU costs when the dataset contains a large number of samples. The proposed algorithms have much alleviated the pain of Bayesian methods in large-scale computing.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057

Friday, January 25, 2019 at 10:00 AM

Hui Quan, Ph.D.
Associate VP & Global Head, Methodology Group, Department of Biostatistics and Programming, Sanofi

Title: Considerations on trial design and data analysis of multi-regional clinical trials

Abstract: Extensive research has been conducted in the Multi-Regional Clinical Trial (MRCT) area. To effectively apply an appropriate approach to a MRCT, we need to synthesize and understand the features of different approaches. In this presentation, numerical and real data examples are used to illustrate considerations regarding design, conduct, analysis and interpretation of result of MRCTs. We compare different models as well as their corresponding interpretations of the trial results. We highlight the importance of paying special attention to trial monitoring and conduct to prevent potential issues associated with the final trial results. Besides evaluating the overall treatment effect for the entire MRCT, we also consider other key analyses including quantification of regional treatment effects within a MRCT and the assessment of consistency of these regional treatment effects.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057

Friday, FEBRUARY 8, 2019 at 10:00 am

Yanxun Xu, Ph.D.
Assistant Professor, Department of Applied Mathematics & Statistics, Whiting School of Engineering, Johns Hopkins University

Title: ASIED: A Bayesian Adaptive Subgroup-Identification Enrichment Design 

Abstract: Developing targeted therapies based on patients’ baseline characteristics and genomic profiles such as biomarkers has gained growing interests in recent years. Depending on patients’ clinical characteristics, the expression of specific biomarkers or their combinations, different patient subgroups could respond differently to the same treatment. An ideal design, especially at the proof of concept stage, should search for such subgroups and make dynamic adaptation as the trial goes on. When no prior knowledge is available on whether the treatment works on the all-comer population or only works on the subgroup defined by one biomarker or several biomarkers, it’s necessary to estimate the subgroup effect adaptively based on the response outcomes and biomarker profiles from all the treated subjects at interim analyses. To address this problem, we propose an Adaptive Subgroup-Identification Enrichment Design, ASIED, to simultaneously search for predictive biomarkers, identify the subgroups with differential treatment effects, and modify study entry criteria at interim analyses when justified. More importantly, we construct robust quantitative decision-making rules for population enrichment when the interim outcomes are heterogeneous. Through extensive simulations, the ASIED is demonstrated to achieve desirable operating char- acteristics and compare favorably against the alternatives.  

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057

FRIDAY, FEBRUARY 22, 2019 AT 10:00 AM

Steven G. Heeringa, Ph.D.
Senior Research Scientist and Associate Director, Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor

Title: Neuroscience on a Population Scale: Design, Measurement, Data Integration and Analysis

Abstract: Neuroscience has a strong research tradition that employs experimental and observational studies in laboratory settings and controlled testing and evaluation in both clinical, educational and volunteer populations. In the past two decades, there has been increasing interest in conducting population-scale epidemiological studies of early age brain development and functioning as well as later age neurological functioning including cognitive impairment, dementias and Alzheimer’s disease. The data collected in these population-based studies is not restricted to observations on neurological systems and functioning but is collected in parallel with a wide array of information on participants’ life events, medical history, social and environmental exposures, genetics and genomics. This rich array of observational data has the potential to greatly advance our understanding of how complex neurological systems develop, are modified by internal or external factors or otherwise change over the life course. The growing field of epidemiological research also presents many challenging problems in design, measurement, data integration and analysis that those of us trained in biostatistics, bioinformatics and biomathematics will be called on to help to solve.

This presentation will use two cases studies to illustrate the nature of the statistical challenges in conducting population-scale neuroscientific research, describe current best practices and outline opportunities for future research. The first case study will be the Adolescent Brain Cognitive Development project (ABCD, https://abcdstudy.org), a 12-year longitudinal investigation of brain morphology and functional development in U.S. adolescents and teens. The second case study will focus on the challenges in design, measurement and analysis faced in special supplemental investigations of dementia and Alzheimer’s disease conducted under the auspices of the larger Health and Retirement Study (HRS, https://hrs.isr.umich.edu/about). Each case study review will include a description of the specific study challenges and current solutions. The major aim of this presentation is to increase awareness of these emerging lines of research and to promote interest on the part of the next generation of statisticians and data scientists who will be called upon to advance the various methodologies that will be required to better understand complex neurological systems and how they relate to our individual attributes and the world around us.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057

Friday, MARCH 22, 2019 AT 10:00 AM

Guest Speaker, Ph.D.

Title: 

Abstract:    

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057

Friday, APRIL 12, 2019 at 10:00 am

Abera Wouhib, Ph.D.
Program Chief, Statistical Methods in Psychiatry Program, Adult Psychpathology & Psychosocial Interventions Research Branch, Division of Translational Research (DTR), NIH

Title:  Estimation of Heterogeneity Parameters in Multivariate Meta-Analysis

Abstract:  Similar to its univariate counterpart, multivariate meta-analysis is a method to synthesize multiple outcome effects by taking in to account the available variance-covariance structure. It can improve efficiency over separate univariate syntheses and enables joint inferences across the outcomes. Multivariate meta-analysis is required to address the complexity of the research questions. Multivariate data can arise in meta-analysis due to several reasons.  The primary studies can be multivariate in nature by measuring multiple outcomes for each subject, typically known as multiple-endpoint studies, or it may arise when primary studies involve several comparisons among groups based on a single outcome or measures several parameters. Although it possesses many advantages over the more established univariate counterpart, multivariate meta-analysis has some challenges including modelling and estimating the parameter of interests. Under random-effects model assumption, we discuss the methods of estimating the heterogeneity parameters and effect sizes of the multivariate data and its application by using illustrative example and simulation results. 

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057

Friday, APRIL 26, 2019 AT 10:00 AM

Ming Yuan, Ph.D.
Professor, Department of Statistics, Columbia University

Title: Quantitation in Colocalization Analysis: Beyond "Red + Yellow = Green

Abstract:  "I see yellow; therefore, there is colocalization.” Is it really so simple when it comes to colocalization studies? Unfortunately, and fortunately, no. Colocalization is in fact a supremely powerful technique for scientists who want to take full advantage of what optical microscopy has to offer: quantitative, correlative information together with spatial resolution. Yet, methods for colocalization have been put into doubt now that images are no longer considered simple visual representations. Colocalization studies have notoriously been subject to misinterpretation due to difficulties in robust quantification and, more importantly, reproducibility, which results in a constant source of confusion, frustration, and error. In this talk, I will share some of our effort and progress to ease such challenges using novel statistical and computational tools.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd. NW, Washington, DC 20057