Webinar Series

LiDS Webinar and Short Course Series

The following webinars are sponsored by the Lifetime Data Science Section.  These are part of the web-based lectures hosted by the ASA.  To register for any upcoming webinars, please visit: www.amstat.org/ASA/Education/Web-Based-Lectures.aspx


To be announced.


Mediation Analysis with Failure Time OutcomesInstructor: Linda Valeri, Columbia University, NY, USADate and Time: Friday, March 3, 2023, 10:00 a.m. – 12:00 p.m. ET (This course will be taught via Zoom)Registration Deadline: Friday, March 3, 2023, at 9:30 a.m. Eastern timeCourse Description:The course covers recent developments in causal mediation analysis for failure time outcomes and provide practical tools to implement these techniques. Mediation analysis concerns assessing the mechanisms and pathways by which causal effects operate. The course covers the relationship between traditional methods for mediation in epidemiology and the social sciences and new methods in causal inference. For time-to-event outcomes, we discuss when the standard approaches to mediation analysis are valid. Using ideas from causal inference and natural direct and indirect effects, alternative mediation analysis techniques are described when the standard approaches do not work. The no-confounding assumptions needed for these techniques are described. SAS, R, and Stata macros to implement these techniques are covered. The methods will be illustrated by an application.Part 1 - Background and Review of Methodology---------------------------------------------- An introduction to mediation analysis under the counterfactual framework- Natural direct and indirect effects in a survival analysis context- Regression approaches for direct and indirect effects in the presence of failure time outcomes- Inference and software for mediation analysisPart 2 – Application and Extensions---------------------------------------------- Application: Evaluating interventions to reduce racial disparities in cancer survival- Extensions: Mediation analysis in the presence of semi-competing risks



Registration Fees:Lifetime Data Science Section Members: $20ASA Members: $30Student ASA Member: $25Nonmembers: $45Access InformationAfter registering, you will receive a confirmation email. In the body of the confirmation email under the “Additional Information” header is information on how to access the webinar. Save this email or register with Zoom right away.

Survival Models for Spatial Data
: Benjamin M. Taylor, Lancaster University, UK
Date and Time: Tuesday, October 18, 2022, 11:00 a.m. – 1:00 p.m. ET (This course will be taught via Zoom)
: Lifetime Data Science Section

Registration Deadline: Tuesday, October 18, at 10:30 a.m. Eastern time

Course Description:
Statistical methods for the analysis of survival data are not only applicable in the medical context, but also in many other areas of science and engineering. When survival times are spatially-referenced, some evidence of clustering of high or low times might be apparent on a visual inspection of the data. The question naturally arises as to whether these observed spatial survival patterns can be explained by incorporating appropriate covariates into the model or whether, in order to obtain reliable inferences for model parameters of interest, it is necessary to explicitly model the unexplained spatial variation. In this short course, we give an overview of spatial survival analysis: models, inference, and software and cover three applications of these techniques to real-world challenges.

Part 1 - Background and Review of Methodology
- An introduction to the modeling of spatial stochastic processes
- Combining spatial modeling with survival analyses
- Model selection
- Flexible parametric survival models
- Inference and software for fitting spatial survival models
- Extensions

Part 2 - Applications
Application 1: Modelling Survival in HIV Cohorts in Malawi
Application 2: Modelling Survival from Colorectal Cancer in Malaysia
Application 3: Spatial Modelling of Emergency Service Response Times

: Analysis of Composite Time-to-event Outcomes

Presenter: Lu Mao, Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison

Date and Time: June 8, 2022, 12:00 p.m. – 2:00 p.m. ET (This course will be taught via Zoom)

Sponsor: ASA Lifetime Data Science Section

Course Description:
This course provides an overview of the new statistical methodology for the analysis of composite time-to-event outcomes. These outcomes combine death and (possibly recurrent) nonfatal events, such as hospitalization, tumor progression, or infection, and are routinely used as the primary efficacy endpoint in modern phase-III clinical trials. The traditional approach to composite outcomes focuses on time to the first event, whichever type it is, using standard univariate survival analysis techniques. Recent years have seen a surge of more sophisticated and versatile methods, attracting the attention of both statisticians and practitioners. Examples of such methods include the win ratio (Pocock et al., 2012) and its various extensions, the restricted mean time in favor of treatment (a generalized restricted mean survival time), the event (or loss) rate ratio while alive, generalized semiparametric proportional odds regression models, and so on. They improve upon the traditional time-to-first-event analysis in (1) proper prioritization of death over nonfatal events; (2) fuller utilization of multiple/recurrent events; (3) clear and interpretable definition of effect-size estimands; and (4) flexible modeling of different outcome types. In the meantime, a number of user-friendly R-packages that implement the aforementioned methodology have become available. This short course will provide a survey of these methodological developments, along with some practical guidance on using the associated R-packages for real data analysis.

Short course: Joint Modeling of Longitudinal and Survival Data
Instructors: Dimitris Rizopoulos, Professor of Biostatistics, Erasmus University Medical Center, Rotterdam, The Netherlands
Sponsor: ASA Lifetime Data Science Section
Date and Time:  Thursday, March 24, 2022, 11:00 a.m. – 2:00 p.m. ET (This course will be taught via Zoom)

Course Description:  In follow-up studies, different types of outcomes are typically collected for each subject. These include longitudinally measured responses (e.g., biomarkers) and the time until an event of interest occurs (e.g., death, dropout). These outcomes are often separately analyzed, but on many occasions, it is of scientific interest to study their association. This research question has given rise to the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First when the focus is on a survival outcome, and we wish to account for the effect of endogenous time-dependent covariates measured with error, and second when the focus is on the longitudinal outcome, and we wish to correct for non-random dropout. This short course will introduce when these models should be used in practice, which are the key assumptions behind them, and how they can be utilized to extract relevant information from the data.

Time permitting, the following topics will be covered:

  • Review of Relative Risk and Mixed Models: Definitions, Cox and linear mixed models, how to fit them in R
  • The Basic Joint Model: Definition of joint models, assumptions, estimation, comparison with time-dependent Cox model, connection with missing data
  • Extensions of the Basic Joint Model: Functional form, Multiple longitudinal outcomes, multiple failure times
  • Special topics: Dynamic predictions for the survival and longitudinal outcomes

This course assumes knowledge of basic statistical concepts, such as standard statistical inference using maximum likelihood and regression models. Also, a basic knowledge of R would be beneficial but is not required.

Registration: www.amstat.org/education/web-based-lectures#JMLSD
Registration Deadline: Wednesday, March 23, at 12:00 p.m. ET
 LiDS Section Members: $20
 Regular ASA Members: $30
 Students ASA Member: $25
 Nonmembers $45

Each registration is allowed one web connection. Sound is received via audio streaming from your computer's speakers.

Access Information

After registering, an automated email confirmation will be sent to you with instructions on how to access the webinar.

Short course: Causal Time-to-Event Analysis
Stijn Vansteelandt (Ghent University)
                     Oliver Dukes (Ghent University, University of Pennsylvania)
                     Torben Martinussen (University of Copenhagen)
Dates and Times (This course will be taught via Zoom on 2 consecutive days): 
  Part 1: November 29, 2021: 12:00 - 2:30 p.m. ET
      Part 2:
November 30, 2021: 12:00 - 2:30 p.m. ET

Course Description:  Evaluating treatment effects using observational data increasingly requires adjustment for high-dimensional set of covariates in order to control confounding. This is the result of a lack of comparability between treated and untreated subjects in possibly many (pre-treatment) factors that are also related to outcome. Such adjustment is routinely achieved via parametric modelling in a manner that is often not directly targeting the causal question of interest. This can lead to bias (e.g., due to model misspecification or poor detection of confounders), inefficiency and invalid inference (e.g., as a result of ignoring the uncertainty induced by variable selection). In this course, we will review targeted strategies for inferring the causal effect of an exposure on a time-to-event endpoint subject to censoring. On the first day, we will review efficient analysis strategies for randomised experiments, as well as robust methods for analysing observational data. Special attention will be given to the problem of selection and modelling of variables that are sufficient to adjust for confounding and to render censoring non-informative; we will in particular introduce double/triple variable selection strategies, as well as targeted learning techniques. On the second day, we will introduce instrumental variable methods for time-to-event data, as well as methods for inferring causal mechanisms based on the mediational g-formula as well as natural effect models. Software demonstrations in R will be used throughout the course.
The course is aimed to be accessible to applied statisticians, epidemiologists, and other quantitative researchers already familiar with standard survival analysis methods as well as with the language of potential/counterfactual outcomes and with identification (i.e., exchangeability or ignorability assumptions), for example for the Average treatment effect.

Title: ASA Lifetime Data Science Section Career Development Webinar
Cost: Free for LiDS members

Date and Time:
Wednesday, October 20, 2021, 12:00 p.m. - 1:30 p.m. Eastern Time

Do you want to increase your chances of success in publishing your statistics papers? Learn how to obtain grant funding for your research program? Then you won’t want to miss this Career Development webinar sponsored by the ASA Lifetime Data Science (LiDS) section!

You will be able to hear valuable advice and tips from two leading statisticians in the field about how to get your methodological papers accepted by the top statistics journals and write competitive grant proposals for the NIH and other funding agencies.  There will also be break-out sessions in which you will have opportunities to interact directly with the speakers and participants to share experiences and ask questions.  The webinar is free and only for LiDS members, but space is limited so please register early to secure your spot!


Malka Gorfine, PhD., is a Professor in the Department of Statistics and Operations Research at Tel Aviv University, Israel. Her research interests are in disease risk estimation and prediction using candidate genes and environmental risk factors from both family and case-control survival data; statistical genetics; non-parametric statistics; biostatistics; and machine learning. She was Co-Editor of Biometrics from 2017-2019; Associate Editor of Biometrics from 2009-2016, and a member of the Editorial Board of Lifetime Data Analysis from 2013-2016. 

Jeremy Taylor, PhD., is a Professor of Biostatistics and of Radiation Oncology at the University of Michigan. He is a Fellow of the American Statistical Association and recipient of the Mortimer Spiegelman Award from the American Public Health Association, the Michael Fry Award from the Radiation Research Society, and the Jerome Sacks Award from the National Institute of Statistical Sciences. His research interests include longitudinal and survival analysis, cure models, missing data, smoothing methods, clinical trial design, and surrogate and auxiliary variables. He was Editor of Biometrics from 2012-2014, a permanent member of the NIH Biostatistical Methods and Research Design Study Section (BMRD) from 2007-2012, and Chair of BMRD from 2010-2012. 

Doug Schaubel, Ph.D. (Moderator) is a Professor of Biostatistics at the University of Pennsylvania Perelman School of Medicine. His methodologic research interests include survival analysis, recurrent event data, causal inference, and evaluation of time-dependent treatments.  He is a Fellow of the American Statistical Association and received the Excellence in Research Award in 2015 from the University of Michigan School of Public Health. He is currently Associate Editor for Biometrics, Statistics in Medicine, Statistics in Biosciences, and Lifetime Data Analysis. He is also a Statistical Reviewer for JAMA Network Open.

: Survival Outcome Data with High-Dimensional Predictors: Methods and Applications

Presenter: Dr. Yi Li, Professor of Biostatistics, University of Michigan
Date and Time: Thursday, June 24, 2021, 1:00 p.m. - 5:00 p.m. Eastern Time

In the era of precision medicine, survival outcome data with high-throughput covariates and predictors are often collected. These high dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability because of overfitting. This short course will introduce various cutting-edge methods that handle survival outcome data with high dimensional predictors. I will cover statistical principles and concepts behind the methods, and will also discuss their applications to real medical examples.

Time permitting, the following topics will be covered:

  1.  Survival analysis overview: basic concepts and models, e.g. Cox, Accelerated Failure Time (AFT), and Censored Quantile Regression (CQR) Models;
  2. Survival models with high dimensional predictors (p>n): Regularized methods and Dantzig selector;
  3. Survival analysis with ultra-high dimensional predictors (p>>n): Screening Methods, e.g, Principled sure independent screening (PSIS), Conditional screening, IPOD, Forward selection, etc;
  4. Inference for survival models with high dimensional predictors (p>n).

The audience only needs to have some basic knowledge of regression analysis and survival analysis. The relevant papers and software for this short course can be found at: www-personal.umich.edu/~yili/resindex.html

Title: Competing Frameworks and Methods for Competing Risks Data
Presenter: Dr. Douglas Schaubel, Professor at the University of Pennsylvania Perelman School of Medicine
                   Chair-Elect, ASA LiDS Section
Date and Time: Friday, April 30, 2021, 12:00 p.m. - 2:00 p.m. Eastern Time

Competing risks data arise frequently in clinical and epidemiologic studies. Such data are characterized by a survival time that terminates due to one of several mutually exclusive causes. This webinar will cover the following: the two most commonly adopted frameworks for competing risk data; relevant estimands and estimators within each framework; the role of censoring as a competing risk; available modeling strategies; and causal inference in the competing risk setting. The main ideas will be illustrated through several real-data examples.

Statistical Learning with Time-to-Event Outcome
Dr. Noah Simon, University of Washington
Date and Time
: Tuesday, January 26, 2021, 12:00 p.m. - 2:00 p.m. Eastern Time

We will discuss contemporary methodologies for statistical learning with time-to-event outcomes. We will discuss techniques for engaging with high-dimensional data, as well as methods appropriate for data of more modest dimensions with larger numbers of observations. We will look at modern software packages for statistical learning in these contexts, as well as the validation of these predictive models. Time permitting, we will also touch on deep learning with time-to-event data.

During this session we aim to engage with loss-based estimation for the time-to-event outcome; penalized regression; tree-based-survival models; as well as uses of cross-validation with kernel-weighted Kaplan-Meier estimation to evaluate/calibrate a statistical-learning-based model. We will engage with these methodologies using a number of practical examples.