Seminar Series: Recent Past Seminars

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.

Recent Past Seminars:  Fall 2016 - Fall 2017  (Printable PDF)

Fall 2017 Seminars

Spring 2017 Seminars

Fall 2016 Seminars



Friday, september 8, 2017 at 10:00 AM

Hao Wang, Ph.D.
Associate Professor of Oncology, Division of Oncology - Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, John's Hopkins Medicine

Title: Bias and Freezing Effect in Truncated Randomized Clinical Trials

Abstract: Despite the wide use of the design with statistical stopping guidelines to stop a randomized clinical trial early for efficacy, there are unsettled debates of potential harmful consequences of such designs. These concerns include the possible over-estimation of treatment effects in early-stopped trials, and a newer argument of a “freezing effect” that will halt future RCTs on the same comparison since an early-stopped trial represents an effective declaration that randomization to the un-favored arm is unethical. We determine the degree of bias in designs that allow for early stopping, and assess the impact on estimation if indeed future experimentation is “frozen” by an early-stopped trial. We discuss methods to correct for the over-estimate in early-stopped trials. We demonstrate that superiority established in a RCT stopping early and designed with appropriate statistical stopping rules is likely a valid inference, even if the estimate may be slightly inflated. 

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

Friday, SEPTEMBER 22, 2017 at 10:00 AM

Jianhui Zhou, Ph.D.
Associate Professor, Department of Statistics, University of Virginia

Title: A Single Index Model for Censored Quantile Regression

Abstract: Quantile regression has been getting more attention recently in survival analysis due to its robustness and interpretability, and is considered as a powerful alternative to Cox proportional hazards model and accelerated failure time (AFT) model. Allowing a nonlinear relationship between survival time and risk factors, we study a single index model for censored quantile regression, and employ B-spline approximation for estimation. To avoid estimation bias cause by censoring, we consider the redistribution-of-mass to obtain a weighted quantile regression estimator. For high dimensional covariates, dimension reduction approach is adopted to alleviate the “curse of dimensionality". Furthermore, we penalize the developed estimator for variable selection. The proposed methods can be efficiently implemented using the existing weighted linear quantile regression algorithm. The asymptotic properties of the developed estimators are investigated, and their numerical performance is evaluated in simulation studies. We apply the proposed methods to dataset from a kidney transplant study.

Location: Proctor Harvey Amphitheater, Med-Dent Building C105
3900 Reservoir Rd. NW, Washington, DC 20057

Friday, OCTOBER 13, 2017 at 10:00 AM

Feng Cheng, Ph.D. 
Assistant Professor, Department of Pharmaceutical Science, College of Pharmacy; Department of Epidemiology & Biostatistics, College of Public Health, University of South Florida - Tampa

Title: Identify Taxonomic Profiles of the Salivary Microbiome Associated with Type 1 Diabetes

Background: The oral cavity contains a diverse microbiome with over 700 bacterial species, many of which influence human health.
Objective: We hypothesize that features of the salivary microbiome will distinguish gingival health from disease and that these attributes will be more prevalent in those with Type 1 Diabetes (T1D). 1) Characterize the composition of the salivary microbiome from 16s sequencing. 2) Identify features of the salivary microbiome that distinguish those with T1D from those without T1D.
Methods: Passive drool saliva samples and clinical data were obtained from 197 (97 with T1D and 100 without diabetes) adults attending the 12-year visit for the CACTI study.  Salivary DNA was extracted and 16S amplicons were sequenced. 16S reads will be mapped and clustered into operational taxonomic units (OTUs). Multi testing analysis was used to identify associations between the taxonomic microbial profiles and T1D status.
Results: At the phylum level, the main constituents of the salivary microbiome in both T1D and non-T1D were Bacteroidetes, Firmicutes, and Proteobacteria. However, we did find a significant increased abundance of Firmicutes in the saliva from T1D subjects (29%) compared to non-T1D subjects (25%, false-discovery rate (FDR)-adjusted p=0.019). At the genus level, the relative abundances of several genera were higher or lower in T1D compared to non-diabetics. The relative abundance of Prevotella was lower, and the relative abundances of Campylobacter and Streptococcus were higher in those with T1D compared to those without.
Conclusion: The composition of the salivary microbiome was largely made up with Bacteroidetes, Firmicutes, and Proteobacteria. There is association between taxonomic Profiles of the Salivary Microbiome and Type 1 Diabetes. At the phylum level, T1D were enriched with Firmicutes compared to non-T1D. At the genus level, the relative abundances of several genera were higher or lower in T1D compared to non-diabetics.

Location: New Research Building Auditorium
3800 Reservoir Rd. NW, Washington, DC 20057

Friday, OCTOBER 27, 2017 at 10:00 AM

Michael Stoto, PhD
Professor, Department of Health Systems Administration and Population Health, School of Nursing & Health Studies, Georgetown University

Title: Meta-analysis for Drug Safety Assessment: Promises and Pitfalls

Abstract:  Meta-analysis has increasingly been used to identify adverse effects of drugs and vaccines, but the results have often been controversial. In one respect, metaanalysis is an especially appropriate tool in these settings. Efficacy studies are often too small to reliably assess risks that become important when a medication is in widespread use, so meta-analysis, which is a statistically efficient way to pool evidence from similar studies, seems like a natural approach. But, as the examples in this paper illustrate, different syntheses can come to qualitatively different conclusions, and the results of any one analysis are usually not as precise as they seem to be. There are three reasons for this: the adverse events of interest are rare, standard meta-analysis methods may not be appropriate for the clinical and methodological heterogeneity that is common in these studies, and adverse effects are not always completely or consistently reported. To address these problems, analysts should explore heterogeneity and use randomeffects or more complex statistical methods, and use multiple statistical models to see how dependent the results are to the choice of models.

Location: New Research Building Auditorium
3800 Reservoir Rd. NW, Washington, DC 20057

Friday, NOVEMBER 10, 2017 at 10:00 AM

Eric Vance, PhD
Director, Laboratory for Interdisciplinary Statistical Analysis (LISA); Associate Professor, Department of Applied Mathematics, University of Colorado Boulder

Title: Building a Global Network of Statisticians and Collaboration Laboratories

Abstract: Statistics, analytics, and data science provide powerful methods, tools, and ways of thinking for solving problems and making decisions, but not everyone who could benefit from applying statistics and data science to their research has the knowledge or skills to apply it correctly. The Laboratory for Interdisciplinary Statistical Analysis (LISA) is a statistical collaboration laboratory recently create at the University of Colorado Boulder that generates, applies, and spreads new knowledge throughout the state, the nation, and the world. LISA’s mission is to train statisticians to become interdisciplinary collaborators, provide research infrastructure to enable and accelerate high impact research, and engage with the community in outreach activities to improve statistical literacy. LISA has learned how to create statistical collaboration laboratories to train students to become effective statistical collaborators and to provide research infrastructure for the university. LISA is spreading this knowledge globally through the LISA 2020 Program to help scientists, government officials, businesses, and NGOs in developing countries discover local solutions to local problems through collaborations with statisticians from newly created statistical collaboration laboratories. The LISA 2020 goal is to build a global network of 20 statistical collaboration laboratories in developing countries by 2020. So far seven stat labs have been created in developing countries to train students to become effective interdisciplinary collaborators and enable researchers and government officials to solve problems and make better decisions.

Location: Proctor Harvey Amphitheater, Med-Dent Building C105
3900 Reservoir Rd. NW, Washington, DC 20057


SEMINARS from spring 2017:

Friday, April 28, 2017 at 10:00 AM

Yuelin Li, Ph.D.
Associate Attending Behavioral Scientist, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center

Title: Neurocognitive Impairment After Cancer, Aggression Under Stress, and Supreme Court Justices' Voting: What Do They Have in Common?

Abstract: The Rasch Model (RM) is a classic IRT (Item Response Theory) model in psychometrics. RM is used to solve various applied problems including the measurement of a psychological construct, the scoring of patient-reported outcomes, and in understanding the politics in the high court. I will begin with the measurement of aggression as an example on how to fit a Rasch Model using Gibbs sampling. Next, the RM can be extended to quantify the politics of the Supreme Court. These first two examples will be brief. I will spend most of the time investigating a paradox—that many cancer survivors report memory deficits, yet their memory seems intact by standard neurocognitive tests. A Bayesian latent regression RM (Li, et al. 2016) helps to make sense of this apparent contradiction. I will provide a practical guide, on how to fit the Bayesian latent RM, and how to use the MCMC chains to derive empirical estimates that are harder to get with non-Bayesian methods. The overall goal is to show how psychometric methods have many useful applications across diverse disciplines. Hopefully, a brief introduction will stir discussions on how IRT models may be useful in your own research.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484

Friday, March 24, 2017 at 10:00 AM

Jenna Krall, Ph.D.
 Assistant Professor, Department of Global and Community Health, College of Health and Human Services, George Mason University

Title: Estimating Sources of Air Pollution and Their Impact on Human Health

Abstract: Exposure to particulate matter (PM) air pollution has been associated with increased mortality and morbidity. PM is a complex chemical mixture, and associations between PM and health vary by its chemical composition. Identifying which sources of PM, such as motor vehicles or wildfires, emit the most toxic pollution can lead to a better understanding of how PM impacts health. However, exposure to source-specific PM is not directly observed and must be estimated from PM chemical component data. Source apportionment models aim to estimate source-specific concentrations of PM and the chemical composition of PM emitted by each source. These models, while useful, have some limitations. Specifically, the models are not identifiable without additional information, the estimated source chemical compositions may not match known source compositions, and the models are difficult to apply in multicity studies. In this talk, I introduce source apportionment models and discuss current challenges and opportunities in their application. I estimate sources and their health effects in two studies: a study of commuters in Atlanta, GA and a multicity time series study of four U.S. cities.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484

Friday, February 24, 2017 at 10:00 AM

Peter Song, Ph.D. 
Professor, Department of Biostatistics, University of Michigan at Ann Arbor

Title: Fusion Learning of Model Heterogeneity in Data Integration

Abstract: As data sets of related studies become more easily accessible, combining data sets of similar studies is often undertaken in practice to achieve a larger sample size and higher power. A major challenge arising from data integration pertains to data heterogeneity in terms of study population, study design, or study coordination. Ignoring such heterogeneity in data analysis may result in biased estimation and misleading inference. Traditional techniques of remedy to data heterogeneity include the use of interactions and random effects, which are inferior to achieving desirable statistical power or providing a meaningful interpretation, especially when a large number of smaller data sets are combined. In this paper, we propose a regularized fusion learning method that allows us to identify and merge inter-model homogeneous parameter clusters in regression analysis, without the use of hypothesis testing approach. Using the fused lasso, we establish a computationally efficient procedure to deal with large-scale integrated data. Incorporating the estimated parameter ordering in the fused lasso facilitates computing speed with no loss of statistical power. We conduct extensive simulation studies and provide an application example to demonstrate the performance of the new method with a comparison to the conventional methods. This is a joint work with Lu Tang.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484

Friday, February 10, 2017 at 9:30 AM

Yinglei Lai, Ph.D.
Professor of Statistics, Department of Statistics, George Washington University

Title: Concordant Integrative Analysis of Multiple Two-Sample Genome-Wide Expression Data Sets

Abstract: The development of microarray and sequencing technologies enables biomedical researchers to collect and analyze large-scale molecular data. We will introduce our recent studies on the concordant integrative approach to the analysis of multiple related two-sample genome-wide expression data sets. A mixture model is developed and yields concordant integrative differential expression analysis as well as concordant integrative gene set enrichment analysis. As the number of data sets increases, it is necessary to reduce the number of parameters in the model. Motivated by the well-known generalized estimating equations (GEEs) for longitudinal data analysis, we focus on the concordant components and assume some special structures for the proportions of non-concordant components in the mixture model. The advantage and usefulness of this approach are illustrated on experimental data.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484

Thursday, February 2, 2017 at 10:45 AM

Kelly H. Zou, PhD, PStat, ASA Fellow
Senior Director and Analytic Science Lead, Real World Data & Analytics, Global & Health Impact

Title: Real-World Evidence in the Era of Big Data

Abstract: Given the desire to enhance the effectiveness and efficiency of health care systems, it is important to understand and evaluate the risk factors for disease progression, treatment patterns such as medication uses, and utilizations such as hospitalization. Statistical analyses via observational studies and data mining may help evaluate patients’ diagnostic and prognostic outcomes, as well as inform policies to improve patient outcomes and to control costs. In the era of big data, real-world longitudinal patient-level databases containing the insurance claims of commercially insured adults, electronic health records, or cross-sectional surveys, provide useful insights to such analyses. Within the pharmaceutical industry, executing rapid queries to inform development and commercialization strategies, as well as pre-specified non-interventional observation studies, are commonly performed. In addition, pragmatic studies are increasingly being conducted to examine health-related outcomes. In this presentation, selective published examples on real-world data analyses are illustrated. Results typically suggest that paying attention to patient comorbidities and pre-index or at index health care service utilization may help identify patients at higher risk and unmet needs for treatments. Finally, fruitful collaborative opportunities exist across different sectors among academia, industry and the government.

Location: Med-Dent C-104, W. Proctor Harvey Amphitheater
3900 Reservoir Rd, Washington, DC 20057-1484

Friday, January 27, 2017 at 10:00 AM

Goodarz Danaei, M.D.
Associate Professor, Department of Epidemiology, School of Public Health, Harvard University

Title:  Observational Data for Comparative Effectiveness Research: An Emulation of Randomized Trials of Statins & Primary Prevention of Coronary Heart Disease

Abstract: This presentation reviews methods for comparative effectiveness research using observational data. The basic idea is using an observational study to emulate a hypothetical randomised trial by comparing initiators versus non-initiators of treatment. After adjustment for measured baseline confounders, one can then conduct the observational analogue of an intention-to-treat analysis. We also explain two approaches to conduct the analogues of per-protocol and as-treated analyses after further adjusting for measured time varying confounding and selection bias using inverse-probability weighting. As an example, we implemented these methods to estimate the effect of statins for primary prevention of coronary heart disease (CHD) using data from electronic medical records in the UK. Despite strong confounding by indication, our approach detected a potential benefit of statin therapy. The analogue of the intention-to treat hazard ratio (HR) of CHD was 0.89 (0.73, 1.09) for statin initiators versus non-initiators. The HR of CHD was 0.84 (0.54, 1.30) in the per-protocol analysis and 0.79 (0.41, 1.41) in the as-treated analysis for 2 years of use versus no use. In contrast, a conventional comparison of current users versus never users of statin therapy resulted in a HR of 1.31 (1.04, 1.66). We provide a flexible and annotated SAS program to implement the proposed analyses.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484

Friday, January 13, 2017 at 9:30 AM

Mei-Ling Ting Lee, Ph.D.
Professor, Department of Epidemiology and Biostatistics; Director, Biostatistics and Risk Assessment Center, University of Maryland at College Park

Title: Threshold Regression Models with Application in a Multiple Myeloma Clinical Trial

Abstract: This presentation reviews methods for comparative effectiveness research using Cox regression methods are well known. It has, however, a strong proportional hazards assumption. In many medical contexts, a disease progresses until a failure event (such as death) is triggered when the health level first reaches a failure threshold. I’ll present the Threshold Regression (TR) model for patient’s latent health process that requires few assumptions and, hence, is quite general in its potential application. We use TR to analyze data from a randomized clinical trial of treatment for multiple myeloma. A comparison is made with a Cox proportional hazards regression analysis of the same data.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484



Friday, November 11, 2016 at 10:00 AM

Keith Muller, Ph.D.
Associate Chair and Professor, Institute for Child Health Policy, University of Florida

Title: Four Statistical Guidelines for Planning Reproducible Research

Abstract: Concerns about reproducibility in science are widespread. In response, the National Institutes of Health has changed review procedures and training requirements for applicants ( The Director of NIH and his deputy outlined their plans in Collins and Tabak (2014). Key methodological concerns include poor study designs, incorrect statistical analyses, inappropriate sample size selection, and misleading reporting. Planners can avoid the concerns by following four statistical guidelines. 1) Explicitly control both Type I errors (false positives) and Type II errors (false negatives). 2) Align the scientific goals, study design, data analysis plan, and the sample size analysis. 3) Vary inputs to the sample size analysis to determine the sensitivity to the values assumed. 4) Account for statistical uncertainty in inputs to sample size computations. Extending the guidelines to sequences of studies requires careful allocation of exploratory and confirmatory analyses (leapfrog designs) and allows some forms of adaptive designs. We give examples in the talk for a variety of designs and hypotheses. Case studies include a randomized drug trial in kidney disease, an observational study of quality of care in Medicaid, and a neurotoxicology experiment in rats. Analytic and simulation results provide the foundation for the conclusions.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484

Friday, October 28, 2016 at 10:00 am

Felix Elwert, Ph.D.
 Associate Professor of Sociology, University of Wisconsin-Madison

Title: Graphical Causal Models

Abstract: This talk introduces the three central uses of directed acyclic graphs (DAGs) for causal inference in the observational biomedical and social sciences.  First, DAGs provide clear notation for the researcher’s theory of data generation, against which all causal inferences must be judged. Second, DAGs reveal to what extent the researcher’s data-generating model can be tested. Third, researchers can inspect the DAG to determine whether a given causal question can be answered (“identified”) from the data. After introducing basic building blocks, we will discuss a number of real examples to demonstrate how DAGs help solve thorny practical problems in causal inference.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484

Friday, October 14, 2016 at 10:00 am

Dennis Lin, Ph.D.
University Distinguished Professor of Statistics, Pennsylvania State University

Title: Dimensional Analysis and Its Applications in Statistics

Abstract: Dimensional Analysis (DA) is a fundamental method in the engineering and physical sciences for analytically reducing the number of experimental variables prior to the experimentation. The principle use of dimensional analysis is to reduce from a study of the dimensions of the variables on the form of any possible relationship between those variables. The method is of great generality. In this talk, an overview/introduction of DA will be first given. A basic guideline for applying DA will be proposed, using examples for illustration. Some initial ideas on using DA for Data Analysis and Data Collection will be discussed. Future research issues will be proposed.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484

FRIDAY, September 23, 2016 AT 10:00 AM

Yongzhao Shao, Ph.D.
Professor, Population Health and Environmental Medicine and Deputy Director of New York University Cancer Institute Biostatistics Shared Resources 

Title: Prognostic Accuracy for Semi-parametric Mixture Cure Models

Abstract: An unmet significant challenge in the treatment of many early-stage cancers is the lack of effective prognostic models to identify patients who are at high risk of disease progression from a large number of potentially cured patients. Semi-parametric mixture cure models can account for latent cure fractions in patient populations thus are more suitable prognostic models than standard survival models such as Cox Proportional Hazard models or Proportional Odds models that ignore the existence of latent cure fractions. Without the requirement of knowing who is surely cured, the semiparametric mixture cure models can be used to evaluate predictive utility of biomarkers on cure probability and on survival of uncured subjects. However, appropriate statistical metrics to evaluate prognostic efficiency in the presence of cured patients have been lacking. In this paper, we introduce concordance-based prognostic metrics for semi-parametric mixture cure models and develop consistent estimates. The asymptotic normality and confidence intervals of these estimates are also established. Finite sample applicability of the developed indices and estimates are investigated using numerical simulations and illustrated using a melanoma data set. This talk is based on joint work with Dr. Yilong Zhang at Merck.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484

FRIDAY, September 9, 2016 AT 10:00 AM

Jianguo Sun, Ph.D.
Professor, Department of Statistics, University of Missouri

Title: Statistical Analysis of Interval-Censored Time-to-Event Data

Abstract: The analysis of failure time data plays an important and essential role in many studies, especially medical studies such as clinical trials and follow-up studies. One key feature of failure time data that separates the failure time data analysis from other fields, is censoring, which can occur in different forms.  In this talk, we will discuss and review a general form, interval censoring, and the existing literature for the analysis of interval-censored data as well as some research topics.

Location: Warwick Evans Conference Room, Building D
4000 Reservoir Rd, Washington, DC 20057-1484