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2024 Workshops

SER is pleased to offer the following 2024 Workshops! Some workshops are being offered virtually, some in person, and a few workshops are being offered in both formats. Review the date and time details below for specifics on each workshop.

Morning Workshops

Workshop Details

In Person:
June 18, 2024
8:30am – 12:30pm

Target Audience: Beginner

A Primer on Quantile Regression for Epidemiologists

Presenters
Aayush Khadka, University of California San Francisco
Jillian Hebert, University of California San Francisco
Amanda Irish, University of California San Francisco

Description
Quantile regression is a powerful method of evaluating how an exposure affects the entire outcome distribution. This is distinct from analyses on how exposures impact the means, which are more common in the epidemiological literature. However, quantile regression remains underused in epidemiology. Our proposed workshop has two aims: 1) introduce participants to quantile regressions with a focus on distinguishing between estimators targeted at the conditional versus marginal outcome distribution; and 2) equip participants to conduct quantile regression analyses in statistical packages such as R or Stata. Our workshop will cover three domains: quantile regression theory, implementing quantile regression analyses in real-world data, and extending quantile regressions to longitudinal data and instrumental variables study designs. Read more

Workshop Details

In Person:
June 18, 2024
8:30am – 12:30pm

Target Audience: Intermediate

Unlocking the Mysteries of Mixed Exposures: Targeted Learning for Robust Discovery and Causal Inference in Epidemiology

Presenters
David McCoy, University of California Berkeley

Description
In epidemiological studies of high-dimensional data with mixed exposures (such as those involving air pollution, pesticides, pharmaceuticals, or nutrition), researchers face the daunting challenge of unraveling nuanced interactions, discerning susceptible subpopulations, and pinpointing optimal exposure thresholds for regulatory purposes. Standard methods struggle with the sheer number of potential interactions and lack the flexibility to discover nuanced (synergistic or antagonistic) relationships, which are intrinsic to mixed exposure studies. Enter the burgeoning field of Targeted Learning (TL), which fuses machine learning, causal inference, and semiparametric statistical theory to answer causal questions with statistical confidence. Read more

Workshop Details

In Person:
June 18, 2024
8:30am – 12:30pm

Target Audience: Intermediate

Modern Causal Mediation Analysis

Presenters
Kara Rudolph, Columbia University
Nima Hejazi, Harvard School of Public Health
Ivan Diaz, New York University Langone Health

Description
Causal mediation analysis can provide a mechanistic understanding of how an exposure impacts an outcome, a central goal in epidemiology and health sciences. However, rapid methodologic developments coupled with few formal courses presents challenges to implementation. Beginning with an overview of classical direct and indirect effects, this workshop will present recent advances that overcome limitations of previous methods, allowing for: (i) continuous exposures, (ii) multiple, non-independent mediators, and (iii) effects identifiable in the presence of intermediate confounders affected by exposure. Emphasis will be placed on flexible, stochastic and interventional direct and indirect effects, highlighting how these may be applied to answer substantive epidemiological questions from real-world studies. Multiply robust, nonparametric estimators of these causal effects, and free and open source R packages (medshift and medoutcon) for their application, will be introduced. Read more

Workshop Details

In Person:
June 18, 2024
8:30am – 12:30pm

Target Audience: Intermediate

Quasi-Experiments in Epidemiology: Difference-in-Differences, Synthetic Control, and Staggered Adoption Designs

Presenters
Lee Kennedy-Shaffer, Vassar College, Department of Mathematics and Statistics

Description
In recent years, quasi-experimental analyses have become particularly important in epidemiologic research, especially on health policy questions. New methods for these analyses have also arisen, especially in the quantitative social sciences literature, and biases and statistical challenges pointed out in previous methods. Read more

Workshop Details

In Person:
June 18, 2024
8:30am – 12:30pm

Target Audience: Beginner

Sequence Analysis: a novel methodology for the analysis of variables that unfold over time

Presenters
Catherine Duarte, Stanford University
Kristina Van Dang, University of Southern California

Description
A persistent challenge in analysis of variables that unfold over time is reducing the thousands of unique trajectories individuals may follow into a smaller subset of similar trajectoriy groups, while retaining substantive differences between groups. Sequence analysis is a powerful and underutilized data-driven approach that solves this critical challenge by empirically calculating trajectory similarities. Originally developed in biology to analyze DNA strings, sequence analysis has been adapted to the social sciences to compare and group lifecourse trajectories such as education, family, or work trajectories accounting for the order, timing, and duration of events. Read more

Workshop Details

In Person:
June 18, 2024
8:30am – 12:30pm

Target Audience: Beginner

Yes you can!...Write a successful K proposal

Presenters
Mercedes Bravo, Duke Global Health Institute, Duke University
Christine Gray, Duke Global Health Institute, Duke University

Description
Writing a K-series NIH proposal is exciting but daunting. We will go over what a K is (and is not), and walk through the entire process, from generating feasible and fundable ideas to navigating the grant submission process. We will distinguish different K mechanisms, focusing on the K01 and K99-R00, and tradeoffs between them. Topics will include aligning aims with training goals, strategies for constructing a mentoring team, budget considerations (including effort and subawards), and how a K fits into a research-oriented career trajectory. We will review the expected format and structure of K proposals, and offer tips on grantsmanship and writing reviewer-friendly proposals. We will cover terminology, as well as the NIH review process and scoring. Read more

Workshop Details

In Person:
June 18, 2024
8:30am – 12:30pm

Target Audience: Beginner

Peerspectives on peer review: A crash course on peer review for major biomedical journals for students and early career researchers

Presenters
Tobias Kurth, Charité – Universtitätsmedizin Berlin
Jess Rohmann, Charité – Universtitätsmedizin Berlin
Timothy Feeney, BMJ
Toivo Glatz, Charité – Universtitätsmedizin Berlin

Description

Understand the roles of peer reviewers, editors, and journals, practice giving concise, constructive feedback, and learn how to present your findings more effectively in this 4-hour workshop. This session is a spin-off of the successful semester-long “Peerspectives” training program at Charité – Universitätsmedizin Berlin and is specifically tailored to students and early career researchers with little or no prior publishing or peer review experience. We will use real examples and share insights from our experience as authors, peer reviewers, and editors at major biomedical journals to introduce you to fundamentals of a good review. Our workshop will be co-led by experienced BMJ editors and early career researchers who have conducted research on using peer review training to improve research methods competencies. Read more

Workshop Details

In Person:
June 18, 2024
8:30am – 12:30pm

Target Audience: Beginner

Git’n up to speed on versioning control

Presenters
Ghassan Hamra, DLHcorp
Ian Buller, DLHcorp
Nat MacNell, DLHcorp
Audrey Brown, DLHcorp

Description

Versioning control is standard is many industries, but severely lacking in public health research. With increasing calls for a more rigorous, reproducible culture in science and code and data sharing requirements, versioning control becomes increasingly important. In fact, versioning control a current system to support reproducible research. Our workshop will briefly touch on the importance of reproducibility in research. Then we will demonstrate how to establish and maintain a Git repository for published research. Read more


Afternoon Workshops

Workshop Details

In Person:
June 18, 2024
1:00pm – 5:00pm

Target Audience: Beginner

Mentor Training for Epidemiologists

Presenters
Brittany Charlton, Harvard Medical School/Harvard T.H. Chan School of Public Health

Description
Although mentoring relationships are critical for academic and career success, mentors are often left to learn how to do their part in these relationships through trial and error. And yet, there is a growing movement to prepare mentors more deliberately. The National Academies of Science, Engineering, and Medicine released a report in 2019 titled ‘The Science of Effective Mentorship in STEMM.’ This report documents the wealth of research on factors that contribute to effective mentorship and ways that mentors can build these skills. The Center for the Improvement of Mentoring Experiences in Research (CIMER) has developed and tested curricula that use this evidence-based approach to promote effective mentorship. Read more

Workshop Details

In Person:
June 18, 2024
1:00pm – 5:00pm

Target Audience: Intermediate

Beyond the ATE: Estimating the causal effects of binary, categorical, continuous, and multivariate exposures in R

Presenters
Kara Rudolph, Columbia University
Ivan Diaz, New York University Langone Health
Nick Williams, Columbia University

Description

Modified treatment policies (MTPs) are a class of interventions that generalize static and dynamic interventions for categorical, continuous, and multivariate exposures. MTPs are hypothetical interventions where the post-intervention is defined as a modification of the natural value of the exposure that can depend on the unit’s history. This short course will introduce the lmtp R package for estimating the causal effects of MTPs in both point-treatment and longitudinal studies. We will discuss identification of MTPs, estimation with a targeted minimum-loss based estimator and a sequentially doubly-robust estimator, and provide guidance on estimator choice. Read more

Workshop Details

In Person:
June 18, 2024
1:00pm – 5:00pm

Target Audience: Intermediate

Causal inference for time-varying (longitudinal) exposures

Presenters
Laura Balzer, UC Berkeley
Nerissa Nance, UC Berkeley

Description
This workshop applies the Causal Roadmap to estimate causal effects with multiple intervention variables, such as the cumulative effect of an exposure over time and the effects on survival-type outcomes with right-censoring. We will cover longitudinal causal models, identification in the presence of time-dependent confounding, and estimation of joint treatment effects using G-computation, inverse probability weighting (IPW), and targeted minimum loss-based estimation (TMLE) with Super Learner. During the workshop session, participants will work through the Roadmap using an applied example and implement these estimators with the ltmle R package. Prior training in causal inference with a single timepoint exposure is highly recommended.

Workshop Details

In Person:
June 18, 2024
1:00pm – 5:00pm

Target Audience: Beginner

Git and GitHub for Public Health Research

Presenters
Corinne Riddell, UC Berkeley
Lauren Wilner, University of Washington

Description

Version control, the practice of tracking and managing changes to statistical code, is essential for reducing errors in a statistical analysis. However, many epidemiologists are not trained to do this and are unsure how it fits with institutional review board (IRB) protocols and privacy standards. In this workshop, we will provide an introduction to git and GitHub, to equip epidemiologists with version control tools that also meet ethical standards. Read more

Workshop Details

In Person:
June 18, 2024
1:00pm – 5:00pm

Target Audience: Intermediate

Epidemiological analysis of electronic health records

Presenters
Neal Goldstein, Drexel University
Milena Gianfrancesco, Pfizer, Inc.

Description
Increasingly data mined from the electronic health record (EHR) are being used for secondary analysis in epidemiological research. But more data does not equate to better quality research. In this workshop, we will cover the fundamentals of working with EHR data and designing and conducting valid epidemiological analyses. The workshop will be half didactic lecture and half interactive group exercise. Participants will work with real EHR data as the basis of the group exercises, and therefore will be required to complete a small pre-workshop training to obtain access to these data. Following the group exercise, all participants will reconvene to discuss the challenges and opportunities of working with real EHR data.

Workshop Details

In Person:
June 18, 2024
1:00pm – 5:00pm

Target Audience: Intermediate

ABC's of M-estimation

Presenters
Paul Zivich, University of North Carolina at Chapel Hill
Rachael Ross, Columbia University
Bonnie Shook-Sa, University of North Carolina at Chapel Hill

Description
M-estimation provides a collection of powerful tools for epidemiologists. The more familiar maximum likelihood estimators are special cases of M-estimators. Importantly for epidemiologic research, they allow for the simultaneous estimation of multiple parameters and a streamlined approach to estimate variances of parameter estimates that depend on other parameters. This latter feature is particularly important for many common methods which involve the estimation of so-called “nuisance” parameters (e.g., the propensity scores for inverse probability weighting). This feature of M-estimators eliminates the need for computationally intensive procedures like bootstrapping. Finally, M-estimators can be implemented programmatically, avoiding the need for manual calculations. Altogether, fortuitous application of M-estimators has the potential to advance the quality and efficiency of epidemiologic analyses. Read more

Workshop Details

In Person:
June 18, 2024
1:00pm – 5:00pm

Target Audience: Intermediate

Causal Inference and Outcome-Wide Studies

Presenters
Tyler VanderWeele, Harvard T. H. Chan School of Public Health

Description
The workshop will provide an overview of the principles of causal inference and the extension of those principles to outcome-wide studies. Outcome-wide studies broaden traditional approaches to assessing the causal effects of an exposure using confounding control, but over numerous outcomes rather than a single outcome. Methodological topics addressed in the workshop will include: (i) temporal and confounding control principles for causal inference; (ii) sensitivity analysis metrics, such as the E-value, to evaluate robustness or sensitivity of effect estimates to potential unmeasured confounding; and (iii) approaches to handle multiple testing. Read more

Workshop Details

In Person:
June 18, 2024
1:00pm – 5:00pm

Target Audience: Beginner

EdSHARe: New Prospective Cohort Data for Research on the Effects of Education on Late-life Cognition

Presenters
Rob Warren, University of Minnesota

Description
The purpose of the workshop is to introduce attendees to two extraordinary new data resources for studying the long-term effects of education and other early-life factors on later-life morbidity, mortality, and cognition. Both long-running cohort studies began as large, nationally representative, and diverse longitudinal studies of education. Both have been repurposed as studies of aging, health, mortality, and cognition by reviving them at midlife and following sample members forward. The Education Studies for Healthy Aging Research (EdSHARe; www.edshareproject.org) project is conducting these two cohort studies. Read more

Workshop Details

In Person:
June 18, 2024
1:00pm – 5:00pm

Target Audience: Intermediate

Causal mediation analysis with multiple mediators using interventional effects

Presenters
Margarita Moreno-Betancur, Murdoch Children’s Research Institute & University Of Melbourne
Ghazaleh Dashti, Murdoch Children’s Research Institute & The University of Melbourne

Description
Many health research questions concern the multiple pathways that are presumed to mediate a relationship between an exposure and an outcome. Very often, the translational intent of such research questions is to inform potential intervention targets. However, the usual causal mediation approaches do not consider this interventional intent and rely on assumptions that are either too stringent or not assessable in practice. Recently an alternative approach has emerged based on “interventional effects” that assess the impact of relevant interventions on one or multiple mediators and are identifiable under relaxed assumptions. This approach is gaining popularity in applications, making it timely to present this topic. Read more

Workshop Details

In Person:
June 18, 2024
1:00pm – 5:00pm

Target Audience: Advanced

Introduction to Monte Carlo Simulation with R

Presenters
Ashley Naimi, Emory University

Description
This course will focus on the design and analysis of Monte Carlo simulation studies. Simulation studies are an invaluable tool in any analyst’s kit. They can facilitate developing a firm understanding of basic and advanced methods concepts, and provide a flexible means of evaluating whether analytical techniques will work as expected under specific conditions. Read more

Evening Workshops

Workshop Details

In Person:
June 18, 2024
5:30pm – 7:30pm

Target Audience: Beginner

Introduction to Difference in Differences Using Stata

Presenters
Chuck Huber, StataCorp LLC

Description
This workshop will briefly introduce the concepts and jargon of difference-in-differences (DID) models and show how to fit the models using Stata’s suite of DID commands. We will demonstrate how to fit models for repeated cross-sectional data using ‘didregress’ and for panel/longitudinal data using ‘xtdidregress’. We will also fit heterogeneous DID models where the average treatment effect varies over time or cohort using ‘hdidregress’ and ‘xthdidregress’. We will discuss the model assumptions and how to check these assumptions after fitting a model. We can check the parallel-trends assumption using ‘estat trendplots’ and ‘estat ptrends’ and we can check for anticipation of treatment using ‘estat granger’. After fitting heterogeneous DID models, we will also demonstrate how to aggregate the average treatment effect among the treated (ATET) using ‘estat aggregation’ and how to visualize the trends in ATETs using ‘estat atetplot’.

Workshop Details

In Person:
June 18, 2024
5:30pm – 7:30pm

Target Audience: Intermediate

Incorporating Diversity & Inclusion: Philosophies, Statements, and Practice

Presenters
Hoda S. Abdel Magid, MHS, PhD, Assistant Professor, Department of Population and Public Health Studies, University of Southern California
Stefania Papatheodorou, MD, PhD, Department of Epidemiology, Harvard TH Chan School of Public Health
Cara Frankenfeld, PhD, Faculty Scientist III, Maine Health Institute for Research

Description
Epidemiologic research and implementation affect people of all races, ethnicities, ages, genders, religions, sexual orientations, disabilities, and economic status.This workshop will elaborate on the significance of D&I in shaping the ethical and philosophical foundations of epidemiology. Instructors will challenge traditional epidemiologic models and paradigms to ensure they account for diverse populations, experiences, and health disparities and provide practical examples on how to incorporate D&I principles into the research process. We will share insights into the importance of D&I statements and best practices for crafting them. Read more

Workshop Details

In Person:
June 18, 2024
5:30pm – 7:30pm

Target Audience: Intermediate

An introduction to directed acyclic graphs: What you never wanted but needed to know about bias and didn't even know to ask

Presenters
Ian Shrier, Centre for Clinical Epidemiology, Lady Davis Institute, McGill University

Description
This workshop will introduce participants to directed acyclic graphs (DAGs). We will review the basic principles and
show how they can be used to determine appropriate sets of variables for estimating total causal effects of
exposure (treatment). Participants will work through concrete examples of increasing complexity. We will also
introduce how DAGs can be used in more advanced applications, including natural and controlled direct and
indirect effects and study design.

Workshop Details

In Person:
June 18, 2024
5:30pm – 7:30pm

Target Audience: Beginner

Causal inference with transfer entropy: An introduction for beginners

Presenters
Roni Barak Ventura, New York University Tandon School of Engineering
Maurizio Porfiri, New York University Tandon School of Engineering
James Macinko, University of California, Los Angeles
Manuel Ruiz Marín, University of Cartagena

Description
In epidemiology, causal inference is commonly performed within a counterfactual framework, where an outcome is examined in treated and untreated groups. Information theory offers powerful means to infer causal interactions between multiple variables from their time-series in the absence of counterfactuals. Specifically, the construct of transfer entropy measures causality as the reduction of uncertainty in predicting the future state of a variable from its present, given additional knowledge about the present or past of another variable. It infers causality in the presence of nonlinear interactions and multiple time-delays. The effectiveness of transfer entropy was demonstrated in a wide variety of applications, ranging from neuroscience to economics to animal behavior to climate change. Read more

Virtual-Only Workshops

Workshop Details

Virtual:
April 22, 2024
10:00 AM  – 2:00 PM MST

Target Audience: Intermediate

An overview of Difference-in-Difference and Synthetic Control Methods: Classical and Novel Approaches

Presenters
Roch Nianogo, UCLA
Tarik Benmarhnia, University of California, San Diego

Description
The interest in and use of quasi-experimental methods to evaluate the effect of a health policy or event on a health outcome has drastically increased in the epidemiological literature. Difference-in-differences (DID) and synthetic control (SC) designs exploit the specific timing and place of an intervention implementation as a natural experiment. Canonical versions of such designs have been typically used in settings including one policy/treatment of interest relying on several identification assumptions. In the past few years, recently developed designs based on staggered DID and SC have been proposed to relax several assumptions and handle multiple time periods and exposed units. Furthermore, many flexible extensions of the SC methods have been proposed such as the generalized synthetic control and the augmented synthetic control. Read more

Workshop Details

Virtual:
April 24, 2024
10:00 AM – 2:00 PM MST

Target Audience: Beginner

An Introduction to R for Epidemiologists

Presenters
Stephen Mooney, University of Washington

Description
This workshop will introduce participants to the R statistical computing platform for use in epidemiologic analysis. It is not intended to transform untested novices into R wizards in a mere half-day; rather, the goal will be to introduce the conceptual underpinnings, tools, and external resources that participants will need to overcome barriers to using R that they might encounter on their own, later. The material is designed for epidemiologists who are already familiar performing analyses using other statistical software (e.g. SAS/Stata/SPSS) but who have no first-hand experience with the R language. More specifically, the course will cover 1) basic R syntax, 2) importing data, 3) constructing, cleaning, and manipulating data objects, 4) loading and using external packages, 5) simple statistical modeling, and 6) graphics. Participants must bring a laptop with R installed; the instructor will be available by email beforehand to assist with R installation if difficulties arise.

Workshop Details

Virtual:
April 25, 2024

Target Audience: Beginner

The importance of theory in epidemiology

Presenters
Usama Bilal, Drexel University
Anjum Hajat, University of Washington

Description
Theoretical frameworks can provide substantial guidance to researchers, assisting them in what questions to ask and how to frame them, what methods to use, and how to collect and interpret data among other things. However, theory is underutilized in epidemiology. In this workshop we will begin by describing why theory matters to epidemiologic inquiry, underscoring its use in all subfields of epidemiology (not just social epidemiology). Then we will provide a brief overview of some of the key theories used in epidemiology (e.g., fundamental cause theory, ecosocial theory and critical race theory), followed by a discussion of studies that use theory particularly well and others where the lack of theory was a missed opportunity. Read more

Workshop Details

Virtual:
May 10, 2024

Target Audience: Beginner

High-dimensional propensity score and its machine learning and double robust extensions in residual confounding control in pharmacoepidemiologic studies

Presenters
Ehsan Karim, The University of British Columbia

Description
Are you an epidemiologist or biostatistician aiming to enhance your skills in the analysis of large health datasets, such as administrative databases? Interested in cutting-edge methods dealing with real-world data, and want to learn about ways to reduce residual confounding bias in pharmacoepidemiology? This workshop is for you. Learn about high-dimensional propensity scores (hdPS) and their machine learning and double robust extensions. Equip yourself for today’s data-rich healthcare landscape. Join us for actionable insights that you can apply to your research immediately. Read more

Workshop Details

Virtual:

June 5, 2024

Target Audience: Beginner

Causal inference with transfer entropy: An introduction for beginners

Presenters
Roni Barak Ventura, New York University Tandon School of Engineering
Maurizio Porfiri, New York University Tandon School of Engineering
James Macinko, University of California, Los Angeles
Manuel Ruiz Marín, University of Cartagena

Description
In epidemiology, causal inference is commonly performed within a counterfactual framework, where an outcome is examined in treated and untreated groups. Information theory offers powerful means to infer causal interactions between multiple variables from their time-series in the absence of counterfactuals. Specifically, the construct of transfer entropy measures causality as the reduction of uncertainty in predicting the future state of a variable from its present, given additional knowledge about the present or past of another variable. It infers causality in the presence of nonlinear interactions and multiple time-delays. The effectiveness of transfer entropy was demonstrated in a wide variety of applications, ranging from neuroscience to economics to animal behavior to climate change. Read more

Workshop Details

Virtual:

July 9, 2024

Target Audience: Beginner

Introduction to Difference in Differences Using Stata

Presenters
Chuck Huber, StataCorp LLC

Description
This workshop will briefly introduce the concepts and jargon of difference-in-differences (DID) models and show how to fit the models using Stata’s suite of DID commands. We will demonstrate how to fit models for repeated cross-sectional data using ‘didregress’ and for panel/longitudinal data using ‘xtdidregress’. We will also fit heterogeneous DID models where the average treatment effect varies over time or cohort using ‘hdidregress’ and ‘xthdidregress’. We will discuss the model assumptions and how to check these assumptions after fitting a model. We can check the parallel-trends assumption using ‘estat trendplots’ and ‘estat ptrends’ and we can check for anticipation of treatment using ‘estat granger’. After fitting heterogeneous DID models, we will also demonstrate how to aggregate the average treatment effect among the treated (ATET) using ‘estat aggregation’ and how to visualize the trends in ATETs using ‘estat atetplot’.

Workshop Details

Virtual:

July 24,2024

Target Audience: Intermediate

What would it take to change your inference? Quantifying the Discourse about Causal Inferences in Epidemiology

Presenters
Kenneth Frank, Michigan State University

Description
Causal inferences are often challenged because of uncontrolled bias. We will turn concerns about potential bias into questions about how much bias there must be to invalidate an inference. For example, challenges such as ‘But the inference of an exposure might not be valid because of pre-existing differences between the treatment groups’ are transformed to questions such as ‘How much bias must there have been due to uncontrolled pre-existing differences to make the inference invalid?’ By reframing challenges about bias in terms of specific quantities, this workshop will contribute to scientific discourse about uncertainty of causal inferences. Critically, the approaches presented in this workshop based on correlations of omitted variables and the replacement of cases have strong intuitive appeal. Read more