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:
July 18, 2022
10:00 – 2:00pm MT
Instructor:
Eric Lofgren
Jeanette Stingone
Target Audience: Beginner
Algorithms, Bootstrapping and Cross-validation: The ABCs of Machine Learning
Machine learning is broadly defined as analytic techniques that fit models algorithmically by adapting to patterns in data. Many epidemiologists wonder how these methods can complement the theoretically-grounded, causal inference approaches more common in our field. This workshop will explore how epidemiologists can use machine learning to advance their research and practice, while reflecting on some of the ethical and scientific considerations that arise from the use of data-driven techniques. Prior to the workshop, attendees will be sent 2-3 readings to introduce general topics. The workshop itself will include didactic lectures that introduce key terms, commonly-used algorithms, evaluation techniques and examples of epidemiologic studies that incorporated machine learning. These will be supplemented by presentations of case-studies and demonstrations of analytic pipelines using R/R Studio. Attendees will participate in hands-on programming exercises of publicly available data, while also discussing the ethical and scientific challenges presented by different research scenarios. At the conclusion of this workshop, attendees will be able to discuss scenarios where machine learning can benefit epidemiologic analysis, analyze public health data using commonly-used algorithms, and feel empowered to pursue additional training or collaborate with scientists with expertise in machine learning.
Workshop Details
Virtual:
July 11, 2022
10:00 – 2:00pm MT
Instructor:
Roch A. Nianogo
Tarik Benmarhnia
Target Audience: Beginner
An introduction to Difference-in-Differences and Synthetic Control Methods for Epidemiogists
The interest in and use of quasi-experimental methods to evaluate the impact of a health policy or program on some disease or outcome of interest has drastically increased in the epidemiological literature. Some designs exploit the specific timing and place of an intervention implementation as a natural experiment. In this context, interrupted time series, difference-in-differences (DID) (and related designs such as staggered DID) as well as synthetic control methods have been used. In this workshop, we propose an overview of different quasi-experimental methods covering the historical context, the identification assumptions under the potential outcomes framework, and the different steps to implement such methods using various case studies. This workshop will introduce the theory and practice on the what, why, and how to implement Difference-in-Difference and Synthetic Control Methods in R/Rstudio. Attendees will work individually on hands-on programming exercises
Workshop Details
An introduction to directed acyclic graphs: What you never wanted but needed to know about bias and didnʼt even know to ask.
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 14, 2022
8:30am – 12:30pm
Virtual:
May 13, 2022
10:00 – 2:00pm MT
Instructor:
Steve Mooney
Target Audience: Beginner
An Introduction to R for Epidemiologists
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
In Person:
June 14, 2022
1:00 – 5:00pm
Virtual:
May 20, 2022
10:00 – 2:00pm MT
Instructor:
Laura Balzer
Lina Montoya
Target Audience: Intermediate
Causal inference for multiple time-point (longitudinal) exposures
This workshop applies the Causal Roadmap to estimate the 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 maximum likelihood 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 time-point exposure is recommended.
Workshop Details
In Person:
June 14, 2022
8:30am – 12:30pm
Virtual:
July 15, 2022
10:00 – 2:00pm MT
Instructor:
Jessica Young
Mats Stensrud
L. Paloma Rojas-Saunero
Target Audience: Intermediate
Causal inference with competing events
A competing risk event is any event that ensures that the outcome of interest cannot subsequently occur. For example, in a study where incident dementia is the primary outcome, death is a competing event because dementia cannot onset after an individual has died. When competing events are present, many possible definitions of a causal effect may be considered. Choosing a causal effect of practical interest requires understanding the interpretation of different counterfactual contrasts and the assumptions needed to identify those contrasts using the study data and subject matter knowledge. This workshop will introduce participants to a counterfactual framework for causal inference in the face of competing events. Participants will learn how to articulate and interpret different types of causal effects when competing events are present, and approaches to estimating them under transparent assumptions with the aid of causal diagrams. In part I, we consider counterfactual contrasts of popular parameters from the competing risks literature including cause-specific and subdistribution hazards, and cause-specific cumulative incidences and consider their relation to total and controlled direct effects from the mediation literature. In part II, we introduce the separable effects, new causal effect definitions that may be of particular clinical relevance in competing events settings. Each part will outline the theoretical concepts through examples and provide hands-on exercises in R.
Workshop Details
In Person:
June 14, 2022
8:30am – 12:30pm
Virtual:
May 16, 2022
10:00 – 2:00pm MT
Instructor:
Kara Rudolph
Nima Hejazi
Ivan Diaz
Target Audience: Intermediate
Causal mediation analysis
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. 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. To ensure translation to real-world data analysis, this workshop will incorporate hands-on R programming exercises to allow participants practice in implementing the statistical tools presented. It is recommended that participants have working knowledge of the basic notions of causal inference, including counterfactuals and identification (linking the causal effect to a parameter estimable from the observed data distribution). Familiarity with the R programming language is also recommended.
Workshop Details
In Person:
June 14, 2022
5:00 – 7:00pm
Virtual:
June 20, 2022
10:00am – 2:00pm MT
Instructor:
Chuck Huber
Target Audience: Beginner
Creating maps and animated maps with Stata
This talk will demonstrate how to create maps and animated maps using the community-contributed grmap command. We will introduce shapefiles, learn how to find them, and show how to merge them with other datasets such as the Johns Hopkins COVID-19 dataset. Along the way, we will review data management tools such as local macros, loops, and merging files. No prior experience with mapping is necessary.
Workshop Details
Virtual:
June 3, 2022
10:00 – 2:00pm MT
Instructor:
Moyses Szklo
Polly Marchbanks
Target Audience: Beginner
Critical Review and Preparation of Manuscripts Reporting Epidemiologic Findings
Overview abstract: 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. Small group work will follow the presentation so that participants can compare their reviews, prepare a consolidated list of critical comments on the paper and a decision letter. Each group will designate a rapporteur, who will present the group’s review of the paper and decision letter to the whole group of participants. At the end of the workshop, students will receive copies of the manuscript’s AJE reviews, the initial editorial decision and the final version of the paper. Attendance will be limited to 30 participants.
Workshop Details
Virtual:
July 8, 2022
10:00 – 2:00pm MT
Instructor:
Lauren Wyatt
Alison Krajewski
Alexandra Larsen
Target Audience: Beginner
Data Visualization for Epidemiology with ggplot2: Mastering Presentation-Grade Figures
Data visualization is critical to conveying new findings from research and is a vital part of advancing the field of epidemiology around the globe. There are a variety of options for creating figures with licensed software, but data visualization packages like ggplot2/R are easily accessible and economical alternatives that can produce high quality, journal-ready figures. The syntax of ggplot2 is challenging to learn, so this workshop aims to allow participants to become comfortable with the syntax, create elegant, complex figures, and be comfortable applying the skills learned to their own research projects. This workshop, led by a diverse, all-female panel of new researchers, will offer examples using ggplot2 in R. This session will begin with a brief introduction to the ggplot2 package and supporting packages. We will cover general practices for manipulating data structures and data formatting for creating ggplots. We will spend the majority of the workshop introducing examples of various plots that are frequently used in epidemiology, focusing on the following aspects: adding confidence intervals to point estimates; manipulating background, axes, titles, legends, colors, themes; creating maps; and saving and exporting high resolution figures. We assume that participants will have some experience in statistical programming. No prior experience with ggplot is necessary, but this workshop is not meant to be an introduction to R. This abstract does not reflect EPA policy.
Workshop Details
In Person:
June 14, 2022
8:30am – 12:30pm
Instructor:
Neal Goldstein
Milena Gianfrancesco
Target Audience: Intermediate
Epidemiological analysis of electronic health records
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 basics of working with EHRs and designing valid epidemiological analyses. The workshop will be half didactic lecture and half interactive group exercises. Participants will provide planned or active research questions in advance, which form the basis of the group exercises. Each small group will identify challenges and opportunities answering the research questions with data from the EHR. Following the breakout groups, all participants will reconvene to discuss the strengths and weaknesses of the research, with specific recommendations offered to ensure success.
The didactic portion of the workshop will include:
1. Designing and analyzing epidemiological studies using EHR data from both inpatient and outpatient settings.
2. Obtaining data from the EHR, including data export, linkage, & variable manipulation.
3. Architecture of the EHR and terminology & data standards.
4. Understanding the clinical population and how this relates to a target/general population.
5. Common pitfalls in working with EHR data and resources for additional reference.
Workshop Details
How to make a picture worth a thousand words: Effectively communicating your research results using statistical graphics
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. Too often, the result is figures that distract, confuse, or even distort data. In this workshop, participants will first learn the fundamentals of effective data visualization. This includes selecting appropriate chart types, drawing attention to the relevant data, using effective visual cues, and providing helpful context. We will discuss how to put these principles into practice, leading viewers to make comparisons, identify trends, and find meaningful correlations. Finally, we will walk through techniques for going beyond the default settings of various software packages to produce well-designed figures.
Workshop Details
Intro to Spatial Analysis & GIS for Spatial Epidemiology in R
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. Spatial analysis is an important tool in uncovering the ways in which where people live, work, and play can influence health outcomes. This workshop will present an introduction to spatial analysis, mapping, and GIScience for health applications & spatial epidemiology using the open source R environment. During this interactive workshop, participants will be introduced to basic concepts in spatial data analysis, generate thematic maps visualizing neighborhood-level health phenomena, geocode and integrate community resource locations (such as health providers, schools, or sources of pollution) for further exploration, and calculate new spatial access variables. We will review how research questions and hypotheses are updated at each stage of exploratory spatial data analysis. Participants should have a basic understanding of the R environment but no experience is necessary with spatial data or R-spatial libraries.
Workshop Details
Virtual:
June 30, 2022
10:00 – 2:00pm MT
Instructors:
Eric Lofgren
Rebecca Smith
Meagan Fitzpatrick
Target Audience: Beginner
Introduction to Infectious Disease Modeling
Infectious disease modeling provides a framework for quantifying disease transmission and dynamic risk, and therefore has been a crucial tool for epidemiologists throughout the response to COVID-19. This workshop will cover the basics of compartmental models, both deterministic and stochastic, how to build and fit them in R, and how to interpret their output for decision-making. The workshop will also involve discussion of the limitations of different modeling approaches and the data requirements for each. In order to maximize experiential learning, the workshop will use a flipped format, with background reading, code in R markdown, and links to short videos will be provided to participants in advance. Reading and videos will cover the theoretical basis for the approaches to be used, define key terms, and provide examples from the epidemiologic literature. During the workshop, participants will work in small groups through a series of hands-on projects applying the approaches within R/R Studio. At the end of the workshop, the attendees will participate in a guided discussion of the practicalities of the models they built, and how the models and their conclusions might be presented to different audiences (such as policy makers or the media). The goal of the workshop is to empower participants to seek additional training, build collaborations with modeling experts, and think critically about the structure, assumptions, and applicability of infectious disease models.
Workshop Details
Introduction to parametric and semi-parametric estimators for causal inference
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 semi-parametric approaches, and 5) interpretation of findings. The focus will be on estimation with a simple substitution estimator (parametric G-computation), inverse probability of treatment weighting (IPTW), and targeted maximum likelihood estimation (TMLE) with Super Learner. Participants will work through the Roadmap using an applied example and implement these estimators in R during the workshop session.
Workshop Details
Mixed-Methods for Epidemiologists to Imporve Causal Inference
There have been calls for epidemiologists to study the biomedical and the social causes of disease, however, it is still unclear how to do it. Mixed-methods (MM) offers a possible solution. This course will focus on the application of MM to identify causes or explain the relationship between a cause and an outcome in observational studies. This course is designed for epidemiologists with none to limited training in MM, and those curious about the benefits of applying these methods to epidemiology. We will begin with an instructional session covering the current paradigms guiding quantitative methods, qualitative methods and MM. We will describe specific applications of MM to epidemiological research with particular attention to causal inference including: 1) identifying new causes and 2) explaining causal mechanisms 3) identifying sources of non-comparability and 4) improving measurement. This portion will cover building causal models using MM, MM study designs and their rationale. The course will also have interactive components including exploring case studies and practicing the integration of quantitative and qualitative data. The course will culminate with participants using our bespoke online tool to design their own MM studies. By completing the course, participants will understand why it is worth taking a MM approach in their future studies of biological and social causes of disease; and they will know how to design a MM study.
Workshop Details
Outcome-wide Epidemiology and Causal Inference
The workshop will provide an overview of the principles of causal inference especially as relevant to outcome-wide studies. Outcome-wide epidemiologic studies are an extension of the approach often used to assess evidence for the causal effects of an exposure using confounding control, but over numerous outcomes rather than a single outcome. Discussion will be given to (i) the temporal and confounding control principles for causal inference in general and as related to outcome-wide studies; (ii) metrics, such as the E-value, to evaluate robustness or sensitivity to potential unmeasured confounding for each outcome; and (iii) approaches to handle multiple testing. Outcome-wide longitudinal designs have numerous advantages over more traditional studies of single exposure-outcome relationships including results that are less subject to investigator bias, greater potential to report null effects, greater capacity to compare effect sizes, a tremendous gain in the efficiency for the research community, a greater policy relevance and a more rapid advancement of knowledge. Discussion will be given to both the practical and theoretical justification for the outcome-wide longitudinal studies and also the pragmatic details of their implementation. Outcome-wide designs have the potential to more rapidly advance our knowledge within epidemiology.
Workshop Details
In Person:
June 14, 2022
8:30am – 12:30pm
Instructor:
Mark van der Laan
Alan Hubbard
Jeremy Coyle
Nima Hejazi
Ivana Malenica
Rachael Phillips
Target Audience: Intermediate
Targeted Learning I: Causal Inference Meets Machine Learning
This workshop will provide an introduction to the field of targeted learning for causal inference, and the corresponding tlverse software ecosystem (https://github.com/tlverse). Emphasis will be placed on targeted minimum loss-based estimation (TMLE) of causal effects under single time point interventions, including extensions for missing covariates and outcomes. These multiply robust, efficient plug-in estimators use state-of-the-art machine learning tools to flexibly adjust for confounding while yielding valid statistical inference. In addition to discussion, this workshop will incorporate both interactive activities and hands-on, guided R programming exercises, to allow participants the opportunity to familiarize themselves with methodology and tools that translate to real-world data analysis. It is highly recommended for participants to have an understanding of basic statistical concepts such as confounding, probability distributions, confidence intervals, hypothesis testing, and regression. Advanced knowledge of mathematical statistics is useful but not necessary. Familiarity with the R programming language will be essential.
Workshop Details
In Person:
June 14, 2022
1:00 – 4:00pm
Instructor:
Mark van der Laan
Alan Hubbard
Jeremy Coyle
Nima Hejazi
Ivana Malenica
Rachael Phillips
Target Audience: Advanced
Targeted Learning II: Advanced Applications of Causal Inference
Building on an introduction to targeted learning and its software ecosystem (the tlverse; https://github.com/tlverse), this workshop serves as a walkthrough of its use for estimation of advanced parameters motivated by causal inference. In particular, we will discuss targeted estimators of the causal effects of dynamic, optimal dynamic, and stochastic interventions; time permitting, estimation of the effects of interventions in settings with time-to-event (survival) outcomes may also be discussed. Throughout, we will draw on advanced uses of machine learning, including conditional density estimation and categorical outcome prediction, highlighting the extensibility of the tlverse. In addition to discussion, this workshop will incorporate both interactive activities and hands-on, guided R programming exercises, to allow participants the opportunity to familiarize themselves with methodology and tools that translate to real-world data analysis. It is highly recommended for participants to have an understanding of basic statistical concepts such as confounding, probability distributions, confidence intervals, hypothesis testing, and regression. Advanced knowledge of mathematical statistics is useful but not necessary. Familiarity with the R programming language will be essential. Prior experience with the tlverse (as covered in the SER workshop “Targeted Learning I”) is highly recommended.
Workshop Details
In Person:
June 14, 2022
1:00 – 4:00pm
Instructors:
Anjum Hajat
Yvette Cozier
Greg Cohen
Target Audience: Beginner
Teaching Epidemiology: Designing Inclusive Curriculum for Equitable Classrooms
Increased interactions with diverse peers enhance students’ educational experiences and bring measurable improvements in learning outcomes for all. Diversity also contributes to the scientific rigor of our scholarship and are necessary for the longevity and robustness of our discipline. Positive classroom climates and teaching practices have been shown to improve persistence and academic and emotional development among diverse students. As instructors, we have a responsibility to level the playing field, so that every student has an equal opportunity to master the learning objectives in our courses.
Building on the wealth of scholarship produced by our colleagues in the social sciences this half-day workshop will employ an active learning approach to developing inclusive classrooms and curricula in Epidemiology (e.g. lectures followed by small group discussions and revising existing syllabi). The following domains are key to the infusion of inclusivity in our courses 1) minding the privilege gap between our students and ourselves when developing our courses, 2) acknowledging and confronting implicit biases, and 3) mitigating stereotype threat in our classrooms.
Specific topics include:
1. Defining diversity and inclusion at your institution
2. Understanding the importance of an inclusive curricula to the field of epidemiology
3. Recognizing and respond to macroaggressions
4. Defining inclusive teaching