Workshops - Time Zone
All workshops are listed in Mountain Time
(Pacific Time – 1, Central Time +1, Eastern Time +2)
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 Read more
Data manipulation, visualization, and reproducible documents with R and the Tidyverse
Recent developments by the R community have revolutionized the data analysis pipeline in R, from manipulating and visualizing data to communicating results. Our workshop will provide hands-on training in tools from the tidyverse ecosystem, using real epidemiologic data. In the first section, we will teach data manipulation with dplyr, a package that makes data cleaning easy, flexible, and enjoyable. Read more
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, Read more
Confounding control for estimating causal effects: Looking under the hood
This workshop introduces concepts of causal inference and confounding control for causal effect estimation. We will introduce potential outcomes, and articulate the conceptual basis and assumptions for two g-methods – standardization via g-computation and inverse probability weighting. Starting with a simple point-treatment setting we will explore how these methods estimate a causal effect, comparing them to more conventional techniques such as multivariable regression and propensity score control. We will then build to the more complex Read more
Critical review and preparation of manuscripts reporting epidemiologic findings
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 Read more
An introduction to directed acyclic graphs: What you never wanted but needed to know about bias and didn’t 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 Read more
E-values, Unmeasured Confounding, Measurement Error, and Selection Bias
The workshop will consider sensitivity analysis for different forms of bias in epidemiology. It will begin with confounding, focusing on a new metric to evaluate sensitivity to unmeasured confounding called the E-value. The E-value is the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the exposure and the outcome, conditional on the measured covariates, to fully explain away the exposure-outcome association. E-value calculations for risk ratios, outcomes differences, odds ratios, Read more
Causal Mediation: Modern Methods for Path Analysis
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 Read more
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.
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 scientific 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, and 5) interpretation of findings. The focus will be on estimation with a simple substitution estimator (parametric G-computation), inverse Read more
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, Read more
Targeted Learning: Causal Inference Meets Machine Learning
This workshop provides a comprehensive 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 estimators of the causal effects of single timepoint interventions, including extensions for missing covariate and outcome data. These multiply robust, efficient plug-in estimators use state-of-the-art, ensemble machine learning tools to flexibly adjust for confounding while yielding valid statistical inference. In Read more
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 will 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.
Toward Self-Sustaining Public Health Interventions: Can we use Social Business in Epidemiology?
In our reality of fierce competition for research and intervention funding, how else can we test epidemiological interventions to work toward self-sustaining solutions for disease prevention? This workshop will explore the ways epidemiologists can use the idea of Social Businesses – types of businesses developed specifically to help a Read more
This workshop welcomes anyone from students to established researchers or practitioners who have ever wondered if there is room for other pathways beyond grants and government funding to advance our fields. Come join us for a chance to think creatively – all ideas are welcome, no business background or expertise required!
An introduction to Difference-in-Differences and Synthetic Control Methods for Epidemiologists
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, difference-in-differences, interrupted time series, and recent synthetic control methods have been used. In this Read more
Algorithms, Bootstrapping and Cross-Validation: The ABCs of Machine Learning for Epidemiologists
Machine learning, broadly defined as analytic techniques that fit models algorithmically by adapting to patterns in data, is growing in use within epidemiology. 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 Read more
Transportability and Data Fusion in Causal Inference Studies
It is becoming increasingly clear that producing causal estimates from studies with acceptable internal validity is not sufficient to guide interventions and policy analysis for population health. External validity is critical for applying internally valid results from a study population to a target population that may or may not have given rise to the study population. Novel developments in causal inference allow us to give the sufficient and necessary conditions for generalizability and transportability. This workshop will provide accessible theoretical and practical Read more
July 19, 2021
10:00am – 2:00pm MT
Neal D. Goldstein
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 Read more
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.
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. Read more
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 – this will include an exercise that allows participants to transform an existing syllabus and course into a more inclusive classroom
The workshop will feature faculty from different institutions serving as presenters and facilitators.
July 30, 2021
10:00am – 12:00pm MT
Target Audience: Intermediate
Simulation Methods for Power Calculation Using Stata
The first half of this talk will introduce the concepts and jargon of power and sample size calculations such as alpha levels, power, and minimum detectable effect sizes. I do several simple calculations manually and then demonstrate how to replicate these calculations using Stata’s -power- commands. Next I demonstrate how to create tables and graphs for power, sample size, and minimum detectable effect sizes for a range of values. I will Read more
August 16, 2021
10:00am – 2:00pm MT
Target Audience: Beginner
Machine Learning and Artificial Intelligence for Causal Inference and Prediction: A Primer
Epidemiologists and public health practitioners are increasingly being asked to interpret, use, or judge the merits of machine learning and artificial intelligence techniques. Yet current epidemiology programs are lacking in their coverage of these methods. This workshop will seek to provide an accessible primer on the use of machine Read more
The goal of this workshop will be to enable participants to (i) understand the challenges, limitations, and strengths of ML/AI methods, and (ii) implement basic algorithms to answer commonly encountered questions in health care settings. The course will be divided in four parts: (i) general introduction to ML/AI (supervised, unsupervised, and reinforcement learning, parametric versus nonparametric estimation, ML versus AI); (ii) challenges facing ML/AI methods (curse of dimensionality, algorithmic bias, hype); (iii) ML for causal inference (double- versus single robust estimation, sample-splitting, cross-fitting); (iv) ML for prediction (overfitting, training versus validation data, cross-validation, bagging). Throughout, specific algorithms will be introduced with the R programming language, including CART, random forests, gradient boosting, and others.