SER is pleased to offer the following 2023 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.
Workshop Details
Virtual:
March 1, 2023
12:00 – 4:00pm EST
In Person:
June 13, 2023
5:30pm – 7:30pm
Instructor:
Hailey Banack
Target Audience: Beginner
Developing Competencies for Doctoral Students in Epidemiology
Presenters
Laura Rosella, University of Toronto
Katie Lesko, Johns Hopkins University
Hailey Banack, University of Toronto
Description
The overall objective of this two-part workshop is to develop a set of specific competencies for doctoral students in epidemiology. Competencies refer to a set of topics that doctoral students in epidemiology programs should demonstrate some mastery of prior to completing their training. There exist formal competencies for graduate programs in public health, but there are no standardized criteria for topics to include in doctoral training programs in epidemiology. Read more
“Developing Competencies for Doctoral Students in Epidemiology” is a two part workshop. Part One will be held in a VIRTUAL format on March 1, 2023. Part Two will be held as an IN PERSON workshops on June 13, 2023. Virtual access to part two will not be available.
Workshop Details
Virtual:
April 21, 2023
12:00 – 4:00pm EST
Instructor:
Laura Balzer
Target Audience: Beginner
Introduction to parametric & semi-parametric estimators for causal inference
Presenters
Jennifer Ahern, UC Berkeley
Laura Balzer, UC Berkeley
Description
“This workshop will introduce participants to the Causal Roadmap for epidemiologic questions: 1) clear statement of the research question, 2) definition of the causal model and parameter of interest, 3) assessment of identifiability that is, linking the causal effect to a parameter estimable from the observed data distribution, 4) choice and implementation of estimators including parametric and semiparametric approaches, and 5) interpretation of findings. Read more
Workshop Details
Virtual:
April 24, 2023
12:00 – 4:00pm EST
Instructor:
Chuck Huber
Target Audience: Intermediate
Create Your Own Stata Commands
Presenters
Chuck Huber, StataCorp LLC
Description
“The first half of this workshop will introduce the basics of creating custom Stata commands. You will learn how to store things in memory, where Stata stores things in memory and how to access them, how to handle conditions and branching, and how to use loops. We will then demonstrate how to use these tools with to write custom Stata commands using -program-, -args-, and -syntax-. You will learn how to write commands to perform simple calculations, post-estimation calculations, and create your own graphics commands. Read more
Workshop Details
Virtual:
April 25, 2023
12:00 – 4:00pm EST
Instructor:
Moyses Szklo
Polly Marchbanks
Target Audience: Intermediate
Critical Review and Preparation of Manuscripts Reporting Epidemiologic Findings
Presenters
Moyses Szklo, Johns Hopkins Bloomberg School of Public Health
Polly Marchbanks, American Journal of Epidemiology
Description
In this half-day workshop, participants will critically review a paper as initially
submitted to the American Journal of Epidemiology (AJE), but not yet published. The paper will be sent
to participants in advance of the workshop for their critical review. During the workshop, a
presentation will be made on some of the main points to be considered when preparing or reviewing a
manuscript. Read more
Workshop Details
Virtual:
May 4, 2023
12:00 – 4:00pm EST
Instructor:
Ehsan Karim, The University of British Columbia
Target Audience: Intermediate
High-dimensional propensity score and its machine learning extensions in residual confounding control in pharmacoepidemiologic studies
Presenters
Ehsan Karim, The University of British Columbia
Description
The use of retrospective health care claims datasets is frequently criticized for lacking complete information on potential confounders. Ultimately, the treatment effects estimated utilizing such data sources may be subject to residual confounding. Digital electronic administrative records routinely collect a large volume of health-related information; and many of whom are usually not considered in conventional pharmacoepidemiological studies. Read more
Workshop Details
In Person:
June 13, 2023
8:30 – 12:30pm
Instructors:
Brittany Charlton
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 carry out their part in these relationships through trial and error. And yet, there is a growing movement to prepare mentors more deliberately. Read more
Workshop Details
In Person:
June 13, 2023
8:30am – 12:30pm
Instructor:
Laura Balzer
Target Audience: Intermediate
Causal inference for time-varying exposures
Presenters
Lina Montoya, UNC
Laura Balzer, UC Berkeley
Description
This workshop applies the Causal Roadmap to estimate the causal effects with time-vary exposures, 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 maximum likelihood estimation (TMLE) with Super Learner.
Workshop Details
In Person:
June 13, 2023
8:30 – 12:30pm
Instructor:
Kara Rudolph
Target Audience: Intermediate
Modern Causal Mediation Analysis
Presenters
Nima Hejazi, Harvard
Ivan Diaz, NYU
Kara Rudolph, Columbia University
Description
Causal mediation analysis can provide a mechanistic understanding of how an exposure impacts an outcome, a central goal in epidemiology and health and social 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.
Workshop Details
In Person:
June 13, 2023
8:30 – 12:30pm
Instructor:
Neal Goldstein
Target Audience: Beginner
Epidemiological analysis of electronic health records
Presenters
Milena Gianfrancesco, Pfizer, Inc.
Neal Goldstein, Drexel University
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.
Workshop Details
In Person:
June 13, 2023
8:30 – 12:30pm
Instructor:
Tyler VanderWeele
Target Audience: Intermediate
Interaction Analysis
Presenters
Tyler VanderWeele, Harvard University
Description
This workshop will provide a broad introduction to the topic of interaction. We will discuss interaction on additive and multiplicative scales, and their relation to statistical models (e.g. linear, log-linear and logistic models). Read more
Workshop Details
In Person:
June 13, 2023
1:00 – 5:00pm
Instructor:
Aayush Khadka
Target Audience: Beginner
A Primer on Quantile Regression for Epidemiologists
Presenters
Anusha Vable, University of California, San Francisco
Jillian Hebert, University of California, San Francisco
Aayush Khadka, 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. Read more
Workshop Details
In Person:
June 13, 2023
1:00 – 5:00pm
Instructor:
Marynia Kolak
Qinyun Li
Target Audience: Beginner
Intro to Spatial Analysis & GIS for Spatial Epidemiology in R
Presenters
Marynia Kolak, University of Illinois
Qinyun Li, University of Gothenburg
Description
Measurements of neighborhood social determinants of health are increasingly urgent in modern public health thinking, and are thought to drive and/or reinforce racial, social, and spatial inequities. Sometimes this necessitates an investigation of neighborhood health patterns, like premature mortality at the census tract scale. Sometimes we’re interested in area factors like poverty, access to affordable housing, distance to the nearest health provider, or polluting factories — and how these factors magnify, moderate, or mediate individual level health.
Workshop Details
In Person:
June 13, 2023
1:00 – 5:00pm
Instructor:
Elise Tookmanian
Maura Kate Costello
Target Audience: Beginner
Media Training: Skills for Communicating Research Findings
Presenters
Elise Tookmanian, National Cancer Institute
Maura Kate Costello, National Cancer Institute
Description
*This workshop is designed for epidemiologists at all career stages.*
The skills needed to effectively communicating research findings require training and practice. This workshop will help epidemiologists improve their ability to effectively communicate their research findings to the media, and through them to the public and specific audiences, such as clinicians, public health practitioners, and other researchers. While this workshop will focus on basic skills of communicating to the media, the skills can also be applied when disseminating information to community partners and other audiences.
Workshop Details
In Person:
June 13, 2023
1:00am – 5:00pm
Instructor:
Louisa Smith
Target Audience: Intermediate
Reproducible Epidemiology in R
Presenters
Louisa Smith, Northeastern University
Description
Have you ever painstakingly copied and pasted output into individual table cells, only to find yourself starting from scratch when you receive an updated dataset from your collaborators? Have you sat twiddling your thumbs while rerunning all of your analyses just because you added a single covariate to a regression model? In this workshop, we will learn techniques for avoiding these frustrating, slow, and error-prone processes. We will focus on conducting reproducible analyses using the targets package in R and on creating reproducible documents using RMarkdown and/or Quarto. Read more
Workshop Details
In Person:
June 13, 2023
1:00 – 5:00pm
Instructors:
Paul Zivich
Target Audience: Intermediate
The ABC's of M-estimation
Presenters
Rachael Ross, University of North Carolina at Chapel Hill
Stephen Cole, University of North Carolina at Chapel Hill
Paul Zivich, University of North Carolina at Chapel Hill
Description
M-estimation is a generalization of maximum likelihood that allows a set of estimating equations to be stacked together and can be used for a variety of epidemiologic measures. Importantly, M-estimators offer a simple way to estimate the variance for complicated measures. Read more
Workshop Details
In Person:
June 13, 2023
5:30 – 7:30pm
Instructor:
Ian Shrier
Target Audience: Beginner
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, 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 13, 2023
5:30 – 7:30pm
Instructor:
Jennifer Cruz
Target Audience: Beginner
Positionality for Epidemiologists: Exploring the Role of Our Social Identities in Quantitative Research
Presenters
Jennifer Cruz, Harvard T.H. Chan School of Public Health
Shoba Ramanadhan, Harvard T.H. Chan School of Public Health
Jarvis Chen, Harvard T.H. Chan School of Public Health
Description
Who we are, who we are in community with, and where we are from all play a role in the science we do- from the research questions we ask to the evidence we use to back our claims. Positionality refers to how our social identities and lived experiences not only influence the choices we make throughout the research process but also how those factors shape how others view us, our work, and the power we hold in a specific research context.
Workshop Details
In Person:
June 13, 2023
5:30 – 7:30pm
Instructor:
Mike Jackson
Target Audience: Beginner
How to make a picture worth a thousand words: Effectively communicating your research results using statistical graphics
Presenters
Mike Jackson, EpiAssist, LLC
Description
Epidemiologists can use statistical graphics to understand our data and to guide us toward correct inferences. Well-designed graphics can also be powerful tools for communicating our study findings. However, while statistical software makes it easy to produce certain types of figures, the default options leave much to be desired. Read more
Workshop Details
In Person:
June 13, 2023
5:30 – 7:30pm
Instructor:
Gesulla Cavanaugh
Target Audience: Beginner
Eye-Tracking Data Analysis for Epidemiological Context
Presenters
Gesulla Cavanaugh, Nova Southeastern University
Description
Eye-tracking data have grown in popularity in behavioral studies but have recently gained traction in other fields, such as neuroscience and education. While their use in epidemiological studies is underexplored, they are value-adding components for various epidemiological models. Read more
Workshop Details
In Person:
June 13, 2023
1:00pm – 5:00pm
Virtual:
May 2, 2023
10:00am – 2:00pm MT
Instructor:
Malcom Barrett
Target Audience: Intermediate
Writing Reproducible Research in R with Quarto
Presenters:
Malcolm Barrett, Posit
Description
In this workshop, you’ll learn how to use the R programming language to generate completely reproducible research using Quarto, a modern scientific publishing framework. We’ll focus on how these tools can support academic research and data science, allowing you to iterate between code and results quickly. Read more
Workshop Details
In Person:
June 13, 2023
8:30am – 12:30pm
Virtual:
May 16, 2023, 2022
12:00 – 4:00pm EST
Instructors:
Steve Mooney
Target Audience: Beginner
R for Epidemiologists
Presenters
Steve 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.
Workshop Details
In Person:
June 13, 2023
1:00pm – 5:00pm
Virtual:
July 6, 2023
12:00 – 4:00pm EST
Instructors:
Jessica Young
Target Audience: Intermediate
Causal Inference and Competing Events
Presenters
L. Paloma Rojas-Saunero, UCLA
Jessica Young, Harvard Medical School
Mats Stensrud, EPFL, Lausanne
Description
A competing risk event is any event that ensures the outcome of interest cannot subsequently occur. For example, in a study where prostate cancer death is the primary outcome, a fatal stroke is a competing event because an individual cannot die of cancer once they have died of stroke. When competing events are present, many possible definitions of a causal effect may be considered. Read more
Workshop Details
Virtual
July 7, 2023
12:00 – 4:00pm EST
Instructors:
Roch Nianogo
Target Audience: Beginner
An overview of Difference-in-Difference and Synthetic Control Methods: Classical and Novel Approaches
Presenters
Tarik Benmarhnia, University of California San Diego
Roch Nianogo, University of California, Los Angeles
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. Read more
Workshop Details
Virtual:
July 18, 2023
12:00am – 4:00pm EST
Instructors:
Kenneth Frank
Target Audience: Beginner
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. There may be bias due to uncontrolled confounding variables or non-random selection into a sample. 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. In part I, we use Rubin’s causal model to interpret how much bias there must be to invalidate an inference in terms of replacing observed cases with counterfactual cases or cases from an unsampled population. This is extended to logistic regression. In part II, we quantify the robustness of inferences in terms of correlations associated with unobserved variables. Calculations will be presented using the open-source app http://konfound-it.com with links to R and Stata modules. The format will be a mixture of presentation and individual hands-on exploration as well as group work.