Colloquia

Spring 2021

All colloquia will be held virtually.

Information about past colloquia is available here.

Date
Speaker
Affiliation
Title
Wednesday, January 20 Ran Xu UConn Allied Health Identify Contagion Effects in Dynamic Social Networks: A latent-space adjusted approach
Wednesday, January 27 Qihe Tang University of New South Wales Insurance Risk Analysis of Financial Networks Vulnerable to a Shock
Wednesday, Feburary 3 Zhiguo Li Duke University A New Robust and Powerful Weighted Logrank Test
Wednesday, Feburary 10 Christian Galarza Morales SPOL-Ecuador Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution: recurrence, existence and applications
Wednesday, Feburary 17 Sudipto Banerjee UCLA Fielding School of Public Health On Massively Scalable Spatial Process Models for High-Resolution Actigraph Data
Wednesday, Feburary 24 Himchan Jeong Simon Fraser University A non-convex regularization approach for stable estimation of loss development factors
Wednesday, March 3 Sumit Mukherjee Columbia University MATH & STATISTICS JOINT COLLOQUIUM
Motif Counting via Subgraph sampling: A fourth moment phenomenon
Wednesday, March 10 Chongliang (Jason) Luo University of Pennsylvania Privacy-preserving Distributed Algorithms for Heterogeneous Data Integration
Wednesday, March 17 HaiYing Wang UConn UConn & UMass joint colloquium
Imbalanced Data, Negative Sampling, and Nonuniform Log Odds Correction
Wednesday, March 24 Andrii Babii UNC, Economics ECONOMICS & STATISTICS JOINT COLLOQUIUM
High-Dimensional Granger Causality Tests with an Application to VIX and News
Wednesday, March 31 Joseph G. Ibrahim UNC Gillings School of Global Public Health Robert W. Makuch Distinguished Lecture in Biostatistics
The Scale Transformed Power Prior for Use with Historical Data from a Different Outcome Model
Wednesday, April 7 Shing Lee Columbia University, Biostatistics Incorporating patient-reported outcomes in dose-finding clinical trials
Wednesday, April 21 Peter Bickel University of California, Berkeley Pfizer Colloquium
Four Excursions in Genomics
Wednesday, April 28 Garvesh Raskutti University of Wisconsin-Madison Sketching meets tensor estimation: Recent developments in large-scale low-rank problems

Ran Xu, Assistant Professor, Department of Allied Health Sciences, University of Connecticut

Wednesday, January 20, 2021 4:00 p.m. EST, 1-hour duration

Identify Contagion Effects in Dynamic Social Networks: A latent-space adjusted approach

Contagion effects, also known as peer effects or social influence process, have become more and more central to social science, especially with the availability of longitudinal social network data. However, contagion effects are usually difficult to identify, as they are often entangled with other factors, such as homophily in the selection process, the individual’s preference for the same social settings, etc. Methods currently available either do not solve these problems or require strong assumptions. Following Shaliziand Thomas (2011), I frame this difficulty as an omitted variable bias problem, and I propose several alternative estimation methods that have potentials to correctly identify contagion effects when there is an unobserved trait that co-determines the influence and the selection. The Monte-Carlo simulation results suggest that a latent-space adjusted estimator is especially promising. It outperforms other estimators that are traditionally used to deal with the unobserved variables, including a structural equation based estimator and an instrumental variable estimator.

Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=e356e69f9e1f5105cafb5d9b0a3cf4c14
Call-in option: US Toll +1-415-655-0002 Access code: 120 246 5294
Date and time:  Wednesday, January 20, 2021 4:00 p.m. EST
Duration:    1 hour

Qihe Tang, Professor, Director of Research, University of New South Wales, Business School

Wednesday, January 27, 2021 4:00 p.m. EST, 1-hour duration

Insurance Risk Analysis of Financial Networks Vulnerable to a Shock

We conduct a quantitative risk analysis of non-core insurance business of selling protection to financial firms against investment losses due to a shock. A static structural model is constructed, composed of a network of firms who cross-hold each other, multiple primitive assets that are vulnerable to a shock, and an insurer who resides external to the network and speculates in selling protection to the financial firms. Assume that each firm in the network is rational and able to decide how much protection to purchase to optimize its portfolio according to the meanvariance principle. As a result, the shock may impact on the insurer but indirectly through the network. More precisely, the network integration, which refers to the level of exposures of the firms to each other, aspects the way that the shock impacts on this non-core insurance business. Our study finds that the network integration and the shock play an interactive role in the insurance risk: An increase in the network integration can either reduce or amplify the impact of the shock on the insurance risk.

Bio: Dr. Qihe Tang joined the UNSW Business School as a Full Professor under the Strategic Hires and Retention Pathways (SHARP) scheme in July 2017.

After earning his Ph.D. in Statistics from the University of Science and Technology of China in 2001, he has worked at different places in the world including the University of Hong Kong (2001), the University of Amsterdam (2002-2004), the Concordia University (2004-2005), and the University of Iowa (2006-2017). At the University of Iowa, he was promoted to Full Professor in July 2012, and he was conferred the F. Wendell Miller Endowed Professorship in July 2014 in honour of his scholarly work and professional contributions.

Qihe Tang’s expertise centers on extreme value theory for insurance, finance, and quantitative risk management. Recently, he has been working on various topics newly arising from the interdisciplinary area of insurance, finance, probability, and statistics. These topics include: (1) interplay of insurance and financial risks, (2) large credit portfolio losses, and (3) modeling, measuring, and managing catastrophe risks. His research on these topics has been constantly supported by external grants.

Qihe Tang has recently been elected as an editor for Insurance: Mathematics and Economics. Currently, he is also an associate editor for the journals TEST, Applied Stochastic Models in Business and Industry, and Statistics & Probability Letters, and serves on the editorial boards of the journals Risks and Dependence Modeling. He has graduated a number of doctoral students who are now university professors all over the world.

Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=e19956d4df155bc843c4837848b7b788e
Call-in option: US Toll +1-415-655-0002 Access code: 120 481 2108
Date and time:  Wednesday, January 27, 2021 4:00 p.m. EST
Duration:    1 hour

Zhiguo Li, PhD, Associate Professor, Department of Biostatistics and Bioinformatics, Duke University

Wednesday, February 3, 2021 4:00 p.m. EST, 1-hour duration

A New Robust and Powerful Weighted Logrank Test


In the weighted logrank tests such as Fleming-Harrington test and the Tarone-Ware test, certain weights are used to put more weight on early, middle or late events. The purpose is to maximize the power of the test. The optimal weight under an alternative depends on the true hazard functions of the groups being compared, and thus cannot be applied directly. We propose replacing the true hazard functions with their estimates and then using the estimated weights in a weighted logrank test. However, the resulting test does not control type I error correctly because the weights converge to 0 under the null in large samples. We then adjust the estimated optimal weights for correct type I error control while the resulting test still achieves improved power compared to existing weighted logrank tests, and it is shown to be robust in various scenarios. Extensive simulation is carried out to assess the proposed method and it is applied in several clinical studies in lung cancer.

Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=e8089a3a49151ebeca9e9d843821c080a
Call-in option: US Toll +1-415-655-0002 Access code: 120 102 8779
Date and time:  Wednesday, February 3, 2021 4:00 p.m. EST
Duration:    1 hour

Christian E. Galarza, Professor, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Escuela Superior Politécnica del Litoral, ESPOL, Guayquil, Ecuador

Wednesday, February 10, 2021 4:00 p.m. EST, 1-hour duration

Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution: recurrence, existence and applications


We compute doubly truncated moments for the selection elliptical (SE) class of distributions, which includes some multivariate asymmetric versions of well-known elliptical distributions, such as, the normal, Student’s t, among others. We address the moments for doubly truncated members of this family, establishing neat formulation for high order moments as well as for its first two moments. We establish sufficient and necessary conditions for their existence. Further, we propose computational efficient methods to deal with extreme settings of the parameters, partitions with almost zero volume or no truncation. Applications and simulation studies are presented in order to illustrate the usefulness of the proposed methods.


Bio: Dr. Christian Galarza is a professor in the faculty of Mathematics and Natural Science at Escuela Superior Politécnica del Litoral (ESPOL), in Guayaquil, Ecuador. He obtained his PhD degree in Statistics in 2020, from the State University of Campinas, UNICAMP, Brazil, where he obtained his Master degree in Statistics as well. He visited the University of Connecticut from 2018 to 2019 as a research scholar. His research interests include quantile regression, linear/nonlinear mixed-effects models, EM and SAEM algorithms, zero-quantile distributions, scale mixture of skew normal distributions and censored and zero-inflated models.

Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=ec435a5f13b278d09b925ef2d99847736
Call-in option: US Toll +1-415-655-0002 Access code: 120 481 2108
Date and time:  Wednesday, February 10, 2021 4:00 p.m. EST
Duration:    1 hour

Sudipto Banerjee, PhD, Professor and Chair, Dept. of Biostatistics, UCLA Fielding School of Public Health

Wednesday, February 17, 2021 4:00 p.m. EST, 1-hour duration

On Massively Scalable Spatial Process Models for High-Resolution Actigraph Data

Rapid developments in streaming data technologies have enabled real-time monitoring of human activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraphy), have become prevalent. An actigraph unit continually records the activity level of an individual, producing large amounts of high-resolution measurements that can be immediately downloaded and analyzed. While this type of BIG DATA includes both spatial and temporal information, we argue that the underlying process is more appropriately modeled as a stochastic evolution through time, while accounting for spatial information separately. A key challenge is the construction of valid stochastic processes over paths. We devise a spatial-temporal modeling framework for massive amounts of actigraphy data, while delivering fully model-based inference and uncertainty quantification. Building upon recent developments in scalable inference, we construct temporal processes using directed acyclic graphs (DAG) and develop optimized implementations of collapsed Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference. We test and validate our methods on simulated data and subsequently apply and verify their predictive ability on an original dataset from the Physical Activity through Sustainable Transport Approaches (PASTA-LA) study conducted by UCLA’s Fielding School of Public Health.

Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=e713aaae0cd2a03cee60fe95be2adfd07
Call-in option: US Toll +1-415-655-0002 Access code: 120 102 8779
Date and time:  Wednesday, February 17, 2021 4:00 p.m. EST
Duration:    1 hour

Himchan Jeong, PhD, Assistant Professor, Dept. of Statistics and Actuarial Science, Simon Fraser University

Wednesday, February 24, 2021 4:00 p.m. EST, 1-hour duration

A non-convex regularization approach for stable estimation of loss development factors

In this article, we apply non-convex regularization methods in order to obtain stable estimation of loss development factors in insurance claims reserving. Among the non-convex regularization methods, we focus on the use of the log-adjusted absolute deviation (LAAD) penalty and provide discussion on optimization of LAAD penalized regression model, which we prove to converge with a coordinate descent algorithm under mild conditions. This has the advantage of obtaining a consistent estimator for the regression coefficients while allowing for the variable selection, which is linked to the stable estimation of loss development factors. We calibrate our proposed model using a multi-line insurance dataset from a property and casualty insurer where we observed reported aggregate loss along accident years and development periods. When compared to other regression models, our LAAD penalized regression model provides very promising results.

Bio: Dr. Himchan Jeong is an Assistant Professor in the Department of Statistics and Actuarial Science at Simon Fraser University, Canada. He is a Fellow of the Society of Actuaries (SOA) and holds a Ph.D. from the University of Connecticut. He has been actively involved in teaching and conducting research in actuarial science for several years. In recognition for his academic achievements and excellence, he has been awarded the James C. Hickman Scholarship from SOA recently in 2018-2020.
His current research interest is predictive modeling for ratemaking and reserving of property and casualty insurance.

Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=e1ca1f81f7e6101bac1f064ea8fcba33f
Call-in option: US Toll +1-415-655-0002 Access code: 120 464 6340
Date and time:  Wednesday, February 24, 2021 4:00 p.m. EST
Duration:    1 hour


MATH & STATISTICS JOINT COLLOQUIUM
Sumit Mukherjee, PhD, Associate Professor, Dept. of Statistics, Columbia University

Wednesday, March 3, 2021 4:00 p.m. EST, 1-hour duration

Motif Counting via Subgraph sampling: A fourth moment phenomenon

Consider the subgraph sampling model, where we observe a random subgraph of a given (possibly non random) large graph $G_n$, by choosing vertices of $G_n$ independently at random with probability $p_n$. In this setting, we study the question of estimating the number of copies $N(H,G_n)$ of a fixed motif/small graph (think of $H$ as edges, two stars, triangles) in the big graph $G_n$. We derive necessary and sufficient conditions for the consistency and the asymptotic normality of a natural Horvitz-Thompson (HT) type estimator. 

As it turns out, the asymptotic normality of the HT estimator exhibits an interesting fourth-moment phenomenon, which asserts that the HT estimator (appropriately centered and rescaled) converges in distribution to the standard normal whenever its fourth-moment converges to 3. We apply our results to several natural graph ensembles, such as sparse graphs with bounded degree, Erdős-Renyi random graphs, random regular graphs, and dense graphons.

Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=e00066bc1007afb5c93b97165b0af7568
Call-in option: US Toll +1-415-655-0002 Access code: 120 596 8108
Date and time:  Wednesday, March 3, 2021 4:00 p.m. EST
Duration:    1 hour

Chongliang (Jason) Luo, PhD, Postdoctoral Researcher, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania

Wednesday, March 10, 2021 4:00 p.m. EST, 1-hour duration

Privacy-preserving Distributed Algorithms for Heterogeneous Data Integration

When studying real-world data, such as the electronic health records (EHR), data collected from multiple sites enable analyses to be conducted with larger sample sizes and increased generalizability. Many distributed algorithms have been proposed and applied to data integration across multiple sites, by requiring only aggregated data (AD) rather than individual patient data (IPD) for the privacy-preserving reason. However, it is often been ignored that data from different sites are heterogeneous. In this talk, two novel privacy-preserving distributed algorithms (PDA) are introduced for heterogeneous data integration, namely the distributed proportional likelihood ratio model (DPLR) and distributed linear mixed model (DLMM). Both algorithms require each site to communicate some AD once, but obtain estimates that are close or identical to that of pooled analyses. The two algorithms have been applied to study the avoidable hospitalization of pediatric patients across multiple care sites within the Children’s Hospital of Philadelphia (CHOP), and the LOS of COVID-19 hospitalization across multiple databases. 


Bio: Dr. Chongliang Luo is currently a postdoctoral fellow at the Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania. He graduated with a Ph.D. in Statistics at the University of Connecticut. His research interests focus on statistical methodology on data integration, including multi-view data integration, statistical methods on meta-analysis and privacy-preserving distributed statistical learning.

Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=e3429f020003404aeeb675ddb143a8603
Call-in option: US Toll +1-415-655-0002 Access code: 138 205 4549
Date and time:  Wednesday, March 10, 2021 4:00 p.m. EST
Duration:    1 hour

UConn & UMass JOINT COLLOQUIUM
HaiYing Wang, Assistant Professor , Dept. of Statistics, University of Connecticut

Wednesday, March 17, 2021 4:00 p.m. EST, 1-hour duration

Imbalanced Data, Negative Sampling, and Nonuniform Log Odds Correction

We investigate non-uniform negative sampling and estimation for imbalanced data. Imbalanced data is ubiquitous in binary response problems, where the number of events (positive class) is significantly smaller than the number of nonevents (negative class). For this scenario, the available information is at the scale of the number of positive instances instead of the full data sample size, and subsampling the negative class is a common practice to reduce the computation and/or data collection costs. We first derive the asymptotic distribution of a general inverse probability weighted (IPW) estimator and minimize its variance to obtain optimal sampling probabilities. To further improve the estimation efficiency, we propose a likelihood-based estimator by correcting log odds for the sampled data. The improved estimator is applicable to all binary response models with any forms of sampling probabilities, and its asymptotic variance is smaller than that of the IPW estimator. We validate our approach on simulated data as well as a real click-through rate dataset with more than 0.3 trillion instances, collected over a period of a month. Both theoretical and empirical results demonstrate the effectiveness of our method. 


Bio: HaiYing Wang is an Assistant Professor in the Department of Statistics at the University of Connecticut. He was an Assistant Professor in the Department of Mathematics and Statistics at the University of New Hampshire from 2013 to 2017. He obtained his Ph.D. from the Department of Statistics at the University of Missouri in 2013, and his M.S. from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences in 2006. His research interests include informative subdata selection for big data, model selection, model averaging, measurement error models, and semi-parametric regression.

http://haiying-wang.uconn.edu

Event address for attendees: https://umass-amherst.zoom.us/j/91556524756
Date and time:  Wednesday, March 17, 2021 4:00 p.m. EST
Duration:    1 hour

ECONOMICS & STATISTICS JOINT COLLOQUIUM
Andrii Babii, PhD, Assistant Professor, Department of Economics, University of North Carolina, Chapel Hill

Wednesday, March 24, 2021 4:00 p.m. EDT, 1-hour duration

High-Dimensional Granger Causality Tests with an Application to VIX and News

We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the time series data structures we focus on the sparse-group LASSO estimator, which includes the LASSO and the group LASSO as special cases. We establish the debiased central limit theorem for low dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sparse-group LASSO residuals. This leads to valid time series inference for individual regression coefficients as well as groups, including Granger causality tests. The treatment relies on a new Fuk-Nagaev inequality for a class of $\tau$-mixing processes with heavier than Gaussian tails, which is of independent interest. In an empirical application, we study the Granger causal relationship between the VIX and financial news.

Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=e159d8cdef6f4ad6532974d291c41e858
Date and time:  Wednesday, March 24, 2021 4:00 p.m. EDT
Duration:    1 hour

Robert W. Makuch Distinguished Lecture in Biostatistics
Joseph G. Ibrahim, PhD, Alumni Distinguished Professor, Department of Biostatistics, UNC Gillings School of Global Public Health

Wednesday, March 31, 2021 4:00 p.m. EDT, 1-hour duration

The Scale Transformed Power Prior for Use with Historical Data from a Different Outcome Model

We develop the scale transformed power prior for settings where historical and current data involve different data types, such as binary and continuous data, respectively. This situation arises often in clinical trials, for example, when historical data involve binary responses and the current data involve time-to-event or some other type of continuous or discrete outcome. The power prior proposed by Ibrahim and Chen (2000) does not address the issue of different data types. Herein, we develop a current type of power prior, which we call the scale transformed power prior (straPP). The straPP is constructed by transforming the power prior for the historical data by rescaling the parameter using a function of the Fisher information matrices for the historical and current data models, thereby shifting the scale of the parameter vector from that of the historical to that of the current data. Examples are presented to motivate the need for a scale transformation and simulation studies are presented to illustrate the performance advantages of the straPP over the power prior and other informative and non-informative priors. A real dataset from a clinical trial undertaken to study a novel transitional care model for stroke survivors is used to illustrate the methodology.

Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=e6250e6537dbf6733fdbb06a50f26755a
Audio conference only
US Toll: +1-415-655-0002
Access code: 120 817 4522
Date and time:  Wednesday, March 31, 2021 4:00 p.m. EDT
Duration:    1 hour



Joseph Ibrahim, Alumni Distinguished Professor of biostatistics, is principal investigator of two National Institutes of Health (NIH) grants for developing statistical methodology related to cancer and genomics research.

Dr. Ibrahim is also the Director of the Biostatistics Core at UNC’s Lineberger Comprehensive Cancer Center. Dr. Ibrahim is currently the biostatistical core leader for the NIH-funded Breast Cancer Specialized Programs of Research Excellence (SPORE) project. He is also co-PI of the statistical P01 grant titled “Statistical Methods in Cancer Clinical Trials.”

Dr. Ibrahim’s areas of research focus are Bayesian inference, missing data problems, medical imaging analysis and genomics. He received a Doctor of Philosophy degree in statistics from the University of Minnesota in 1988. With more than 26 years of experience working in cancer clinical trials, Ibrahim directs the UNC Center for Innovative Clinical Trials — one of eight Gillings Innovation Labs funded by a gift to the School from Dr. Dennis and Joan Gillings.

Dr. Ibrahim is also the director of graduate studies in the Department of Biostatistics at the Gillings School, as well as the program director of the cancer genomics training grant in the same department.

He has served on several national committees and study sections, including as the section chair of the Section on Bayesian Statistical Science of the American Statistical Association and the Biostatistical Methods and Research Design (BMRD) NIH Study Section. He has also served as the associate editor for several statistical journals, and was the editor of the Journal of the American Statistical Society (JASA) – Application and Case Studies from 2013 to 2015.

Dr. Ibrahim has published more than 340 research papers, mostly in the top statistical journals. He also has published two advanced graduate-level books on Bayesian survival analysis and Monte Carlo methods in Bayesian computation. He is an elected fellow of the International Society of Bayesian Analysis, American Statistical Association, Institute of Mathematical Statistics, Royal Statistical Society, and an elected member of the International Statistical Institute.

Robert Makuch is a Professor in the Department of Biostatistics at the Yale School of Public Health and Director of the Regulatory Affairs Track. A graduate of the University of Connecticut (BA), University of Washington (MA – mathematics), and Yale University (MPhil, PhD), Professor Makuch worked at the National Cancer Institute (NCI) and the World Health Organization’s International Agency for Research on Cancer early in his career. He also worked for six months at the National Cancer Research Center in Tokyo, Japan.

He also was heavily involved in HIV research from the mid 80’s through the early-mid 90’s. He participated on the data monitoring committee for the original AZT vs. placebo randomized clinical trial in AIDS patients, and served on numerous committees for the NCI and the National Institute of Allergy and Infectious Diseases. He also worked closely with the Food and Drug Administration (FDA), developing and implementing more than 200 HIV studies. He also served as a Special Government Employee (SGE) to the FDA. He returned to Yale in 1986, and has worked extensively on methodologic issues in clinical trials and large population-based studies since. Another area of current interest involves detection of rare adverse drug events, especially in the post-marketing environment.

These areas of methodologic research evolved as a result of his continued interest (since the mid 1980s) in regulatory affairs science. In addition, Makuch developed a regulatory affairs track at YSPH for graduate and post-doctoral level students, and over the past 10 years has been the leader of more than 25 training programs for senior delegations of the Chinese Food and Drug Administration. His areas of medical application include cancer, HIV, arthritis, and cardiovascular disease.

In 2003, Makuch received the American Statistical Association Fellow Award for his numerous contributions to the field. In 2008, Makuch was received a Distinguished Alumni Award from the University of Connecticut. In 2012, Makuch was nominated to serve on the University of Connecticut Dean’s Advisory Board for the College of Liberal Arts and Sciences. He also has been a decades-long member of Phi Beta Kappa. He also developed a 5-year biostatistics training program in Japan, in collaboration with the Japanese government. His primary research interests continue to be methodologic issues in the design, conduct, analysis, and interpretation of clinical and large-population/epidemiologic studies. Design and sample size considerations for Phase IV studies is another active research area, in which a new class of hybrid designs has been proposed for scientific and regulatory purposes to detect rare adverse events.


Shing M. Lee, Associate Professor, Department of Biostatistics, Columbia University

Wednesday, April 7, 2021 4:00 p.m. EDT, 1-hour duration

Incorporating patient-reported outcomes in dose-finding clinical trials

Oncology dose-finding clinical trials have traditionally determined the maximum tolerated dose based on toxicity outcomes captured by clinicians. With the availability of more rigorous instruments for measuring toxicity directly from patients, there is a growing interest to incorporate patient-reported outcomes (PRO) in clinical trials to inform patient tolerability. In this talk, I will discuss the methodological aspects of dose-finding clinical trials and the key issues regarding the use of PROs in the setting of dose-finding clinical trials. I will present our proposed three extensions of the continual reassessment method (CRM), termed PRO-CRMs, that incorporate both clinician and patient outcomes. The first method is a marginal modeling approach whereby clinician and patient toxicity outcomes are modeled separately. The other two methods impose a constraint using a joint outcome defined based on both clinician and patient toxicities and model them either jointly or marginally.  
Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=ef5d79ffd69a1d012d2963b5aa0bab616
There is also a call-in option: US Toll +1-415-655-000
Access code: 120 687 7312
Date and time:  Wednesday, April 7, 2021 4:00 p.m. EDT
Duration:    1 hour

Garvesh Raskutti, Associate Professor, Department of Statistics, University of Wisconsin-Madison

Wednesday, April 28, 2021 4:00 p.m. EDT, 1-hour duration

Sketching meets tensor estimation: Recent developments in large-scale low-rank problems

Tensors or higher-order arrays are playing an ever-increasing role in data science and algorithms, both because tensor representation is common in many datasets and because tensor encodings of data frequently arise in many algorithms. Developing computationally efficient and statistically reliable algorithms for tensors remains a significant open challenge due to the unique challenges that tensors present. In this talk, I discuss some of my work on algorithms for large-scale high-dimensional tensor estimation. I focus on a recent method which exploits low-rank structure using the idea of importance sketching to yield both efficient algorithms and optimal statistical performance. I demonstrate through theory, simulation studies and a real-data example that our method can tackle problems far beyond the scale of existing methods and lead to statistically reliable solutions.

 

Bio: Dr. Garvesh Raskutti is an Associate Professor at the University of Wisconsin-Madison in the Department of Statistics. He is also an affiliate for the Departments of Computer Science, Electrical and Computer Engineering and the Wisconsin Institute of Discovery Optimization Group. Prior to starting at UW, he completed a Masters of Engineering at the University of Melbourne in 2008 under the joint supervision of Rodney S. Tucker and Kerry Hinton, and a PhD at UC Berkeley in 2012 under the joint supervision of Martin Wainwright and Bin Yu. His research interests include statistical machine learning, optimization, graphical and network modeling and information theory with applications to systems biology and neuroscience. In particular, his research broadly focuses on challenges that arise in large-scale statistical inference problems. These challenges are especially motivated by problems in systems biology and neuroscience. Existing methods typically suffer from both statistical and computational limitations. Dr. Raskutti focuses on developing methods that address these statistical and computational challenges which leads to: (1) novel algorithms and theoretical insights; and (2) potentially novel insights in both systems biology and neuroscience.
Event address for attendees: https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=e7b3fb88c3964f2cf167aaf217c83df0a
There is also a call-in option: US Toll +1-415-655-0002
Access code: 120 549 4692
Date and time:  Wednesday, April 28, 2021 4:00 p.m. EDT
Duration:    1 hour