Afternoon Half-Day Courses:

Statistical methods for time-to-event data subject to truncation:

Instructors: Jing Qian, University of Massachusetts Amherst

          Time: Afternoon Session

Target Audience: students, practitioners or researchers with an interest in understanding statistical analysis of time-to-event data subject to truncation.

Prerequisites for participants: knowledge of statistical inference; basic knowledge of survival analysis.

Computer and software requirements: a computer/laptop installed with R (and RStudio) is recommended

Truncated time-to-event data arises in various fields, including biomedical sciences, public health, epidemiology, and astronomy. It involves biased sampling where the event time is observed only if it falls within a certain interval. This short course reviews statistical methods for time-to-event data subject to left, right, and sequential truncation, exploring both classical and advanced techniques.

The first half introduces classical risk-set adjustment methods for estimating event time distributions and conducting regression analysis with left-truncated data, with or without additional right censoring. Methods for right-truncated data will also be discussed. The assumption of quasi-independence between truncation and event times, which is crucial for the validity of classical methods, will be emphasized, along with hypothesis tests for assessing this assumption.

The second half covers recent methodological advances for analyzing truncated time-to-event data. Topics include methods for estimation and regression under dependent truncation, sequential truncation in observational cohort studies with complex sampling schemes, and techniques for estimation and regression under sequential truncation. Discussions will be supplemented with real-world data examples. R software will be used to demonstrate the implementation of the techniques.

The following are the outlines of the short course.

Teaching Plan for a half-day course entitled “”Statistical methods for time-to-event data subject to truncation””
(using morning time as an illustration)

8:30-9:30am, Part I: Introduction to time-to-event data subject to truncation, highlighting the difference between censoring and truncation. Classical risk-set adjustment methods for estimating event time distribution with left-truncated time-to-event data, with or without additional right censoring. Estimation of event time distribution with right-truncated time-to-event data.

9:30-10:15am: Part II: Regression analysis with left-truncated and right-censored time-to-event data, including Cox model and accelerated failure time model. Regression analysis with right-truncated data.

10:15-10:30am: 15 minutes break

10:30-11:45am: Part III: Hypothesis tests for assessing quasi-independence between truncation and event times. One-sample estimation and regression analysis methods under dependent truncation.

11:45am-12:30pm: Part IV: The concept of sequential truncation in observational cohort studies with complex sampling schemes. Methods for estimating event time distributions and performing regression analysis in the presence of sequential truncation.

Short Bio: Dr. Jing Qian is a Professor of Biostatistics in the Department of Biostatistics and Epidemiology at the University of Massachusetts Amherst, with extensive experience in statistical methodology and its applications to public health and biomedical research. Dr. Qian’s research focuses on the development of statistical methods for survival analysis of biomedical outcomes subject to complex censoring or sampling, biomarker evaluation and risk prediction, and covariates subject to censoring and truncation. His collaborative research spans neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases, breast cancer epidemiology, and health services research. He has served as the Principal Investigator on multiple NIH-funded grants and has published extensively in leading statistical and biomedical journals. Dr. Qian is also an experienced educator, having taught graduate-level courses on introductory, intermediate, and advanced biostatistical methods for over a decade.

Dr. Jing Qian is an experienced educator, having taught graduate-level courses on introductory, intermediate, and advanced biostatistical methods for over a decade at the University of Massachusetts Amherst. His teaching portfolio includes introductory and applied biostatistics courses for public health students, such as Introduction to Biostatistics and Intermediate Biostatistics; intermediate-level theory and methods courses for biostatistics graduate students, such as Fundamentals of Probability and Statistical Inference and Topics in Health Data Science; and advanced statistical theory and methods courses for Ph.D. students in biostatistics, such as Applied Statistical Learning and Advanced Statistical Inference. In all of these settings, Dr. Qian emphasizes the challenge and importance of engaging students and fostering active learning in the classroom.

Beyond classroom teaching, Dr. Qian has served as the primary dissertation advisor to more than 10 postdoctoral research fellows, doctoral students, and master’s students.

Category: Methodology and application

Introduction of Dynamic Borrowing in Clinical Trials and Regulatory Submission:

Instructors: Jerry Li, BMS; Ivan Chan, BMS; Inna Perevozskaya, BMS; Hao Sun, BMS

Time: Afternoon Session

Target Audience: Statisticians working on clinical trials

Prerequisites for participants: NA

Computer and software requirements: NA

Clinical trials represent a significant portion of drug development in both budget and duration. While randomized clinical trials are still gold standard, given the availability of the sheer amount of prior clinical trial data and real-world data/evidence, finding innovative ways to design more efficient clinical trials and to supplement relevant data for regulatory submission have become imperative and led to the increasing popularity of dynamic borrowing.

Dynamic borrowing can bring significant benefits to expedite drug development and to a company’s portfolio. Specifically, dynamic borrowing can overcome challenges when patients are difficult to enroll, reduce the size/duration/risk of a new trial ensuring adequate power, have great operational and cost saving benefits, and boost the power and improve the efficiency of analysis for a trial with a limited sample size.

This short course will cover the common sources of data for borrowing and introduce the approaches of both frequentist and Bayesian borrowing as well as the regulatory landscape in this space.

The following are the outlines of the short course.

The first part of this short course will introduce the rationale and overall benefits of dynamic borrowing, The first part will also cover the general methods. After the break, the second part will be more specific about the methods, regulatory landscape, and possible case studies and demo of an R Shiny App.

Short Bio: Dr. Jerry Li is currently a Director and TA Lead of Hematology Malignant Myeloid Diseases in Global Biostatistics and Data Sciences (GBDS), BMS. Jerry leads statistical support for the clinical development of multiple assets for clinical trials design including phase2/3 seamless design, interactions with worldwide health authorities, and life cycle management of the assets. Jerry established and co-lead the Dynamic Borrowing Working Group at BMS. Dr. also recently co-organized and co-moderated a whole-day Biostatistics Research and Innovation Network (BRAIN) meeting at BMS dedicated to dynamic borrowing topic.

Prior to BMS, Jerry was at Merck and Daiichi Sankyo following working at the FDA. He has held positions with increasing responsibilities in multiple therapeutic areas including oncology, neurosciences, immunology, and infectious disease and demonstrated a track record of successful regulatory approvals.

In addition to dynamic borrowing, Jerry is also interested in dose optimization, phase 2/3 seamless design, statistical modeling of disease-modifying treatment effect, and properties of log-rank test following covariate-adaptive randomization in oncology trials. Jerry received his Ph.D. in statistics from the University of Maryland, College Park.

Dr. Ivan Chan has more than 25 years of experience in the pharmaceutical industry. He is currently a VP and interim Head of Global Biometrics & Data Sciences at Bristol Myers Squibb. Prior to joining BMS, Ivan was VP and Head of Statistical Sciences at AbbVie leading multiple therapeutic areas. In addition, he spent 21 years previously at Merck Research Laboratories where he led the global statistical support for vaccines and early oncology.

Ivan received his B.S. in Statistics from the Chinese University of Hong Kong and Ph.D. in Biostatistics from the University of Minnesota. He is an elected Fellow of the American Statistical Association (ASA) and an elected Fellow of the Society for Clinical Trials (SCT). Ivan was the 2021 recipient of the Deming Lecturer Award from ASA for his outstanding contributions to vaccine development. He currently serves as Executive Director of the International Society for Biopharmaceutical Statistics and Co-Chair of Deming Conference on Applied Statistics. Ivan has previously served as the President of the International Chinese Statistical Association and the Program Chair of the ASA Biopharmaceutical Section. He has 90+ publications in statistical and clinical journals.

Dr. Inna Perevozskaya is a Fellow of the American Statistical Association and a Senior Director and Senior Biometrics Fellow, Head of Statistical Methodology at BMS, and co-lead of dynamic borrowing working group at BMS.

Dr. Hao Sun is currently a senior manager in Global Biostatistics and Data Sciences (GBDS), BMS. Hao received his PhD in Statistics from Iowa State University in 2022. In addition to supporting clinical trials at BMS, Hao is a co-lead of Methodology and Tools subteam within Dynamic Borrowing Working Group at BMS. He is also involved in research of dose optimization. Hao has successfully mentioned several summer interns in dynamic borrowing and dose optimization.

During his PhD, Hao was involved in multiple reach projects including providing high-dimensional mixed graphical model, establishing the consistency of graph reconstruction under complex survey sample designs, and developing design-based BIC for neighbor selection with group lasso to recover the true neighborhood as well as optimizing survey pseudo composite likelihood with coordinate gradient descent to estimate edge parameters. Other projects that Hao was working on include road change detection, Shiny App development for National Resource Inventory, and mixture responses for small area estimation.

Category: Methodology

Comparing Python and R for Data Visualization:

Instructors: Jose Manuel Magallanes, Pontificia Universidad Catolica del Peru

Time: Afternoon Session
Target Audience: Basic to Intermediate Python/R users
Prerequisites for participants: Basic R and Python (not necesarily both)
Computer and software requirements: Laptop is useful to complete exercises. R and Python installed, or access to GoogleColab and RStudio Cloud

The short course will introduce the plotting capabilities of Python and R for tabular, network and geographical data.
The presentation includes a some basic rules of thumb for data visualization. Then, we will compare GGPLOT2 in R with ALTAIR in Python. For network data it will be emphasized the capabilities of both Python (networkx and others ) and R (mainly igraph) to highlight relevent players and connections, and communities. Finally, we will work on shapefiles, paying special attention to projections and principles behind choropleth maps using both R (sf) and Python (geopandas).
The session will make basic use of GitHub for storing and replicate our work.

The following are the outlines of the short course.

Part 1: Presentation
a. Principles of Data Viz
b. Review of Tools to be used
c. Installations needed
Part2. Tabular Data Visualization
a. Univariate plot (CATegorical and NUMerical)
b. Bivariate Plot (CAT-CAT / NUM-NUM/ CAT-NUM)
c. Multivariate plots
Part3. Network data
a. Formatting data as networks.
b. Computing and visualizing relevant nodes
c. Exploring communities
Part4. Geographical Data
a. Maps and Projections
b. Chroropleths and data discretization

Short Bio: Professor Magallanes has two Doctoral degrees. One in Computational Social Science from George Mason University, and another one in Psychology from Universidad Nacional Mayor de San Marcos (UNMSM). He also holds a Master degree in Political Science and Public Management from PUCP, and a BSc in Computer Science from UNMSM. He has received multidisciplinary training on computational approaches on governance matters from University Michigan (ICPSR), National University of Australia, National University of Singapore (ISS), University of Chicago (Argonne NL), Carnegie Mellon University (CASOS), and Harvard Kennedy School.

Professor Magallanes is a Full Professor at the Departement of Social Sciences – Pontificia Universidad Catolica del Peru (PUCP). He is also a part-time Professor at the Universidad Nacional Mayor de San Marcos (UNMSM), and a Lecturer at the The Data Analytics and Computational Social Science (DACSS) program in the School of Public Policy at UMass Amherst. He has been a visiting professor at the University of Washington, and a visiting scholar at Duke, Getulio Vargas Foundation (Brazil), and Universidad de los Andes (Colombia).

Category: Methodology

Win Statistics (Win Ratio, Win Odds, and Net Benefit): Theories and Applications:

Instructors: Gaohong Dong, Sarepta Therapeutics;

Time: Afternoon Session
Target Audience: Statisticians, PhD students, and Statistical researchers in academia, industry, and government
Prerequisites for participants: NA
Computer and software requirements: NA

Over the past decade, the win ratio (Pocock et al. 2012), the win odds (Dong et al. 2019), and the net benefit (Buyse 2010)−as the ratio, odds, and difference of win proportions, respectively−have been comprehensively studied. The three win statistics hierarchically analyze prioritized multiple outcomes. Compared to the traditional “time to first event” analysis for multiple time-to-event outcomes, the win statistics allow the prioritization of multiple outcomes and effectively conduct a “time to worst event” analysis, which can be clinically more meaningful. Moreover, win statistics can incorporate multiple endpoints of same or mixed data types (e.g., time-to-event, ordinal, …), can handle repeated events, semi-competing risks, and non-proportional hazards situations.
The win ratio and the stratified win ratio (Dong et al., 2018) have been applied in the design and analysis of Phase III clinical trials, and have supported regulatory approvals, such as tafamidis and Attruby, respectively. The win odds has also been applied in practice.

The following are the outlines of the short course.

Part 1: Introduction and Theoretical Foundations
1. Introduction of win statistics
1.1. Motivation examples and issues of conventional time-to-first-event analyses
1.2. Win ratio, net benefit, and Finkelstein-Schoenfeld test
1.3. Mann-Whitney parameter
1.4. Win odds
2. Point and variance estimators
3. Complement of win statistics

Part 2: Advanced Concepts and Methods
4. Impact of follow-up time and censoring, and IPCW adjustment
4.1. Impact of follow-up time and censoring
4.2. IPCW (inverse-probability-of-censoring weighing) adjustment
4.3. Other adjustments
4.4. Use of win statistics under non-proportional hazards

Break

Part 2: Advanced Concepts and Methods (continued)
5. Stratified win statistics and handing of noncollapsibility
6. Regression analyses
7. Sample size and power calculations

Part 3: Applications and Practical Considerations
8. Applications
8.1. Cardiovascular trials (focusing on the ATTRibute-CM trial)
8.2. COVID-19 trials
8.3. Pediatric benefit-risk
8.4. Evidence synthesis of efficacy outcomes in oncology trials
8.5. Other applications
9. Regulatory perspective of win statistics
10. Software
11. Limitations and advantages of win statistics
12. Summary

Key references:
Finkelstein and Schoenfeld (1999, 2019); Buyse (2010); Pocock et al. (2012); Dong et al. (2016, 2018, 2020a, 2020b, 2020c, 2021, 2023a, 2023b, 2024); Luo et al. (2015); Bebu and Lachin (2016); Oakes (2016); Peng (2020); Brunner, Vandemeulebroecke, and Mütze (2021); Mao et al. (2021, 2022, 2023; 2024); Gasparyan et al. (2021 and 2022); ); Yu and Ganju (2022); Matsouak (2022); Yang et al. (2022); Cui, Dong, Kuan, and Huang (20223); Seifu et al. (2023); Wang, Zhou, Zhang, Kim et al. (2023); Maurer et al. (2018); Redfors et al. (2020); Lopes et al. (2021); Voors et al. (2022); Romiti et al. (2023); Weatherald et al. (2023); Kondo et al. (2023); Freund et al. (2023); Gregson et al. (2023); Barnhart et al. (2024); Pocock et al. (2024); Gillmore et al. (2024).

Short Bio: Gaohong Dong, PhD, has 20 years of experience in the pharmaceutical industry. He is a Director of Biostatistics at Sarepta Therapeutics. Prior to joining Sarepta, he worked at BeiGene and Novartis. Additionally, he worked as a consultant under his own entity of iStats Inc. Gaohong has been supporting drug development in multiple therapeutic areas including rare diseases, solid organ transplant, stem-cell transplant, infection diseases, and oncology. He is a co-author of many highly cited medical papers in transplant. Gaohong is deeply passionate about statistical research. He published peer-reviewed statistical journal papers and book chapters on Bayesian-Frequentist design, adaptive design, missing data imputation, meta-analysis, and composite of prioritized multiple outcomes. In recent years, his research has focused on the win statistics (win ratio, win odds, and net benefit). His research of the stratified win ratio and the win odds have been applied to the design and analysis of clinical trials, including many Phase III studies across multiple disease areas. Notably, the stratified win ratio (Dong et al., 2018) is the primary analysis for the ATTRibute-CM trial, which is the base for the FDA approval of Attruby in November 2024. Gaohong has been an Associate Editor of the Journal of Biopharmaceutical Statistics since 2017, and has served on the Scientific Program Committees for several major statistical conferences such as Regulatory-Industry Statistics Workshop (RISW), ICSA Applied Statistics Symposium, and Statistics in Pharmaceuticals in recent years.

Category: Methodology

Bayesian Machine Learning Methods for Partially Observed Data:

Instructors: Sujit Ghosh, NC State University;

Time: Afternoon Session
Target Audience: graduate students, postdoctoral fellows, early career researchers
Prerequisites for participants: graduate course in mathematical statistics and probability theory
Computer and software requirements: R and JAGS (both available freely on the web)

This short course offers an in-depth exploration of Bayesian Machine Learning (ML) techniques designed to address challenges in data analysis involving data irregularities (e.g., missing values and censored observations). Drawing on methods from the forthcoming second edition of the book titled Bayesian Statistical Methods (co-authored by the instructor), the course will feature cutting-edge methodologies tailored for modern data challenges. The classical ML methods often become difficult to use when faced with data irregularities. The Bayesian hierarchical modeling framework provides seamless integration of imputations (via posterior predictive distributions) and parameter estimation for predictive analytics. Key topics include: (i) High-dimensional regression using Bayesian shrinkage priors (e.g., spike and slab and horseshoe) ; (ii) Non-parametric regression with shape constraint; (ii) Advanced Monte Carlo methods for efficient posterior sampling (e.g., slice, MALA etc.) and (iv) Causal inference for partially observed data (optional and time permitting). Through hands-on examples, participants will learn to implement these techniques on diverse datasets, gaining skills to manage and extract insights from complex, irregular, and high-dimensional data. The course is ideal for researchers, data scientists, and statisticians eager to enhance their understanding of Bayesian methods in practice.

The following are the outlines of the short course.

Teaching Plan for Bayesian Machine learning Methods for Partially Observed Data
Duration: 4 hours (1:30 – 5:30 pm with 15-min break))
Format: Lecture, hands-on coding sessions, and Q&A

Schedule:
Introduction and Overview (25 minutes)
(i) Overview of the course content and objectives.
(ii) Brief introduction to Bayesian approaches in machine learning.
(iii) Tools: R and relevant packages (e.g., rjags, rstan, brms).

1. High-Dimensional Regression with Bayesian Shrinkage Priors (45 minutes)
(i) Introduction to Bayesian shrinkage priors (e.g., spike and slab, horseshoe priors).
(ii) Application to high-dimensional regression problems with missing values.
(iii) Hands-on: Fitting models using R packages (e.g., bas, boomspikeslab, rjags)
Objective: Identify relevant predictors in datasets with high-dimensional covariates.

2. Posterior Summaries of Simulation Based Methods (45 minutes)
(i) Interpreting and summarizing posterior distributions using MC samples.
(ii) Overview of tools for visualization and interpretation.
(iii) Hands-on: Visualization techniques using various plotting packages
Objective: Communicate insights effectively using Bayesian output.

Break (15 minutes)

3. Non-Parametric Regression Techniques (50 minutes)
(i) Generalized Additive Models and Gaussian Process Regression.
(ii) Applications of non-parametric smoothing with shape constraint.
(iii) Hands-on: Fitting generalized additive models using R packages
Objective: Model nonlinear relationships and smooth trends in complex datasets.

4. Elements of Causal Inference for Partially Observed Data (45 minutes)
(i) Bayesian approaches for causal inference under missingness.
(ii) Handling confounding and partial observability.
(iii) Hands-on: Implementing Bayesian causal models using various R packages
Objective: Address causal questions in datasets with incomplete observations.

Closing Q&A and Wrap-Up (15 minutes)
Summary of key takeaways.
Open forum for questions and feedback.
Resources for further learning, including the upcoming second edition of Bayesian Statistical Methods.

Software and Tools
R Packages:
Modeling: bas, BNPqte, boomspikeslab, brms, CausalBNPBook, mgcv, rjags, rstan
Visualization: bayesplot, posterior.

Participants are expected to have R and RStudio installed before the session (https://posit.co/download/rstudio-desktop/). This schedule ensures participants gain both conceptual understanding and practical experience, tailored to the constraints of a half-day format.

Short Bio: Dr. Sujit Ghosh is a Professor in the Department of Statistics at NC State University, where he has dedicated over three decades to advancing statistical methodology and applications. His expertise lies in the analysis of biomedical and environmental data, and he has significantly contributed to the field through his teaching, research, and mentorship. Prof. Ghosh has authored over 150 refereed journal articles, addressing statistical challenges in biomedical sciences, environmental studies, econometrics, and engineering. He co-authored the widely acclaimed textbook Bayesian Statistical Methods, first published in 2019 (and scheduled to release its second edition in 2025). A celebrated mentor and educator, he received the D.D. Mason Faculty Award in 2023 and the Cavell Brownie Mentoring Award in 2014. Most recently, in 2023, he received Distinguished Alumni Award given by the Department of Statistics at UConn. Renowned for his engaging teaching style, Prof. Ghosh has delivered over 200 invited lectures and seminars at prestigious conferences and institutions worldwide. He has also offered numerous short courses and has been a visiting professor at leading global universities. His dedication to advancing statistical science continues to inspire both students and colleagues in the field.

Category: Methodology, Career development

Statistical Inference in Large Language Models:

Instructors: Weijie Su, University of Pennsylvania; Qi Long, University of Pennsylvania; Xiang Li, University of Pennsylvania

Time: Afternoon Session
Target Audience: PhD students and faculty who are interested in generative AI
Prerequisites for participants: NA
Computer and software requirements: NA

Large Language Models (LLMs) have recently stood out as revolutionary AI tools for processing data in the form of text. However, when harnessing their potential for statistical decision-making, it becomes essential to understand the risks of their outputs. Evaluating the uncertainty and confidence levels associated with LLMs presents both challenges and intriguing opportunities for today’s statisticians. The aim of this one-day short course is to equip statisticians with the skills to integrate inferential concepts into the applications and advancement of LLMs. Course topics include: 1) a brief introduction to the fundamentals of LLMs, tailored for those new to transformers and deep learning; 2) a primer on statistical inference techniques specifically for text data using LLMs; and 3) in-depth exploration of LLM applications in medical domains and the broader data science field. By the end of the course, attendees will possess the skills needed to empower LLMs with statistical inference. While this course promises a deep and enriching dive into the confluence of statistics and advanced AI, no prior knowledge of LLMs is required.

The following are the outlines of the short course.

Take the morning session as an example. The time arrangement would be similar for the afternoon session.

Morning Half-Day Course (8:30 a.m. – 12:30 p.m.)

Session 1: Understanding and Building LLM Foundations
8:30 a.m. – 10:15 a.m. (1 hour 45 minutes)

1. Understand the Evolution and Significance of LLMs
– Recognize the evolution and importance of Large Language Models in AI and data processing.

2. Grasp LLM Architectures and Mechanics
– Describe core principles and architectures of LLMs, including transformers and attention mechanisms.
– Learn how text data is processed, tokenized, and embedded in LLMs.

Break
10:15 a.m. – 10:30 a.m. (15 minutes)

Session 2: Challenges, Applications, and Ethics in LLMs
10:30 a.m. – 12:30 p.m. (2 hours)

1. Identify Challenges in LLMs
– Pinpoint common challenges and limitations such as overfitting and bias.
– Appreciate the importance of critically evaluating model outputs.

2. Analyze Real-World LLM Applications
– Evaluate the use of LLMs in healthcare settings, such as processing clinical notes and predicting patient outcomes.
– Identify other applications, including sentiment analysis and recommendation systems.

3. Navigate Ethical and Responsible Use of LLMs
– Recognize potential biases in medical text data and broader implications in data science.
– Advocate for the fair, ethical, and responsible use of LLMs across various domains.

Short Bio: Weijie Su is an Associate Professor in the Wharton Statistics and Data Science Department and, by courtesy, in the Departments of Computer and Information Science and Mathematics at the University of Pennsylvania. He is a co-director of Penn Research in Machine Learning (PRiML) Center. Prior to joining Penn, he received his Ph.D. in Statistics from Stanford University in 2016 and a bachelor’s degree in Mathematics from Peking University in 2011. His research interests span the statistical foundations of generative AI, privacy-preserving machine learning, high-dimensional statistics, and optimization. He serves as an associate editor of the Journal of Machine Learning Research, Journal of the American Statistical Association, Foundations and Trends in Statistics, and Operations Research, and he is currently guest editing a special issue on Statistics for Large Language Models and Large Language Models for Statistics in Stat. His work has been recognized with several awards, such as the Stanford Anderson Dissertation Award, NSF CAREER Award, Sloan Research Fellowship, IMS Peter Hall Prize, SIAM Early Career Prize in Data Science, ASA Noether Early Career Award, and the ICBS Frontiers of Science Award in Mathematics.

Qi Long, PhD, is a Professor of Biostatistics, Computer and Information Science, and Statistics and Data Science at the University of Pennsylvania. The current focus of his research lab is to advance responsible, trustworthy statistical and ML/AI methods for equitable, intelligent medicine, with a particular focus on multi-modal and generative AI such as large language models (LLMs). His methods research has been supported by NIH, PCORI, NSF, and ARPA-H. He is an Executive Editor of Statistical Analysis and Data Mining. He is an elected Fellow of AAAS, ASA, and AMIA.

Xiang Li is a postdoctoral researcher at the University of Pennsylvania, collaborating with Prof. Qi Long and Prof. Weijie Su. He received his Ph.D. in 2023 and B.S. in 2018 from the School of Mathematical Sciences at Peking University. His research lies at the intersection of statistics, stochastic optimization, and machine learning, with a recent focus on large language models. During his Ph.D., he made significant contributions to federated learning, stochastic approximation, online decision-making, and online statistical inference. His work has been featured at leading machine learning conferences, including ICML, ICLR, and NeurIPS, as well as in top journals such as JMLR and AOS.

Category: Methodology

An Outstanding Supervisor: Leading for Motivation, Innovation, and Retention:

Instructors: Claude Petit (Astellas Pharma) in collaboration with the Leadership in Practice Committee (LiPCom) of the Biopharmaceutical section of the ASA.

Time: Afternoon Session
Target Audience: If you lead a team or are considering a supervisory role, this course is for you.

This short course will bring to life the foundational concepts for becoming the ideal supervisor. Attendees will gain a deeper understanding of the essential leadership competencies that will empower them to grow a mentee or direct report, thus enabling them, in turn, to reach their full potential as well. The rewards of this development will cascade through the organization. Participants will learn and understand the expectations and behaviors necessary for becoming a supervisor for whom employees will want to work, increasing their team productivity through an elevated level of engagement. Engagement and fulfillment of employees is achievable when they feel motivated, are challenged to be the best they can be and are able to accomplish more than they thought they could. This course will consist of lecture, videos, and interactive panel discussions where participants will hear from seasoned and successful leaders about how they have learned from their experiences and developed tips and tricks for growing their supervisory skill set. Finally, participants will learn how to measure the right outcomes for enabling sustained growth in this dimension. It is said that employees do not leave companies, they leave supervisors. While many other leadership courses provide advice to statisticians, statistical analysts, and data scientists on how to be effective leaders, this course focuses on the critical role supervisors/professors/advisors play in their employees’ journeys to becoming strong leaders as well as individuals who propose and drive innovative ideas/solutions and effectively implement them. Strong supervisors, model desired employee behaviors, act as sponsors as well as mentors, contribute to their employees’ career satisfaction, support their employees’ work/life balance and generally retain good employees. If you are currently leading a team, managing a group, or considering a supervisory role, this course will help you be more effective.

This short course is being offered in collaboration with the Leadership in Practice Committee (LiPCom) of the Biopharmaceutical section of the ASA.

The following are the outlines of the short course.

This short course will bring to life the foundational concepts for becoming the ideal supervisor. Attendees will gain a deeper understanding of the essential leadership competencies that will empower them to grow a mentee or direct report, thus enabling them, in turn, to reach their full potential as well. The rewards of this development will cascade through the organization. Participants will learn and understand the expectations and behaviors necessary for becoming a supervisor for whom employees will want to work, increasing their team productivity through an elevated level of engagement. Engagement and fulfillment of employees is achievable when they feel motivated, are challenged to be the best they can be and are able to accomplish more than they thought they could. This course will consist of lecture, videos, and interactive panel discussions where participants will hear from seasoned and successful leaders about how they have learned from their experiences and developed tips and tricks for growing their supervisory skill set. Finally, participants will learn how to measure the right outcomes for enabling sustained growth in this dimension. It is said that employees do not leave companies, they leave supervisors. While many other leadership courses provide advice to statisticians, statistical analysts, and data scientists on how to be effective leaders, this course focuses on the critical role supervisors/professors/advisors play in their employees’ journeys to becoming strong leaders as well as individuals who propose and drive innovative ideas/solutions and effectively implement them. Strong supervisors, model desired employee behaviors, act as sponsors as well as mentors, contribute to their employees’ career satisfaction, support their employees’ work/life balance and generally retain good employees. If you are currently leading a team, managing a group, or considering a supervisory role, this course will help you be more effective.

Short Bio: Claude Petit earned her PhD in Biostatistics, concurrent with a medical degree in 1999 from the University of Kremlin Bicêtre (France) where she studied under Prof. Jean Maccario, employing Bayesian methods as applied to clinical trials, specifically involving the study and treatment of schizophrenia. She served as Adjunct Professor in Mathematics & Statistics at the University of Grenoble (1999), Medical University of Paris (2004), and at Ecole Nationale de la Statistique et d’Administration Informatique (ENSAI), she has been a lecturer at the Yale School of Public Health between  2012 and 2024.

Working in the field of statistics since 1994, Dr. Petit has worked at Sanofi-Aventis (formerly Rhone Poulenc Rorer); ESCLI (CRO); Laboratoires Servier; as well as Lincoln (CRO). She joined Boehringer Ingelheim, France as Biostatistics and Programming Head in 2004. After her move to the US in 2007, she served as Executive Director of Biostatistics and then Vice President of Biostatistics and Data management with Boehringer Ingelheim till July 2021. Currently, VP Statistical and Real World Data Science at Astellas, Claude is leading a global team of talented Statisticians and Programmers in US, Europe, Japan and China.

Eternal learner, she has a passion for leadership, growth and teaching. In 2021, she became a certified Executive Coach and funded Creating & Coaching Essential Leaders, LLC to empower one woman at a time.

Category: Career development