Workshop

2020 Workshops

NOTICE

The following workshops are now full. Registration is no longer available.

  • Utilizing electronic health records for epidemiological analysis (Goldstein)
  • Machine Learning for Epidemiologists: A Statistical Learning Approach (Sippy)

You can register for workshops through the full meeting registration, or register separately here.

PRE-CONFERENCE WORKSHOPS - Times listed are Eastern Time (ET)

Dec
4
Fri
Algorithms, Bootstrapping and Cross-Validation: The ABCs of Machine Learning for Epidemiologists
Dec 4 @ 1:00 pm – 5:00 pm

Session Co-Chair: Jeanette Stingone, Columbia University
Session Co-Chair: Eric Lofgren, Washington State University

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. The workshop will use a flipped classroom format to maximize time for discussion and programming activities during the SER workshop. Prior to the workshop, attendees will be sent 2-3 readings and links to 2-3 30 minute videos. These videos will introduce key terms, commonly-used algorithms, evaluation techniques and examples of epidemiologic studies that incorporated machine learning. During the workshop, these topics will be reinforced through a review of concepts, guided discussions, presentations of case-studies and demonstrations of analytic pipelines using R/R Studio. Attendees will work individually and in small groups on 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.

Dec
7
Mon
Estimation and interpretation: Introduction to parametric and semi-parametric estimators for causal inference
Dec 7 @ 12:00 pm – 4:00 pm

Session Co-Chair: Laura B. Balzer, University of Massachusetts
Session Co-Chair: Jennifer Ahern, University of California, Berkeley

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 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.

Dec
10
Thu
Causal inference for multiple time-point (longitudinal) exposures
Dec 10 @ 12:00 pm – 4:00 pm

Session Co-Chair: Laura B. Balzer, University of Massachusetts
Session Co-Chair: Maya L. Petersen, University of California at Berkeley

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, controlled direct effects, and 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). 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 in a single time-point setting is recommended, but not required.

Dec
11
Fri
E-values, Unmeasured Confounding, Measurement Error, and Selection Bias
Dec 11 @ 1:00 pm – 5:00 pm

Session Co-Chair: Maya Mathur, Stanford University
Session Co-Chair: Louisa Smith, Harvard University

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, and hazard ratios will be discussed. The E-value can be calculated in a straightforward way from study results and its use could help unify assessment of unmeasured confounding. The workshop will proceed by describing very recent analogous easy-to-implement approaches to also address differential measurement error and selection bias. We will conclude by presenting recent extensions allowing sensitivity analysis for all three forms of bias. The methods, taken as a whole, will constitute a straightforward comprehensive approach to bias analysis.

Jan
8
Fri
An introduction to transporting treatment effects from randomized clinical trials to clinical practice
Jan 8 @ 12:00 pm – 2:00 pm

Session Chair: Jennifer Lund, University of North Carolina at Chapel Hill

Randomized clinical trials (RCTs) are considered the gold standard for assessing efficacy of new therapies and are required for regulatory approval. However, patients enrolled on trials are often not representative of patients in whom treatment will ultimately be delivered in clinical practice. When response to therapy varies across subgroups, differences between trial and clinical populations can contribute to the “efficacy-effectiveness gap” – where a treatment’s efficacy in a trial differs from its effectiveness in clinical practice. Methods for generalizability and transportability can help bridge this gap. These methods combine RCT and clinical practice data to generate evidence that directly addresses therapy effectiveness in target populations. Such approaches leverage the internal validity of RCTs with the external validity of clinical practice data to better inform real-world decision-making.

In this workshop, we will provide an overview of methods for generalizing and transporting treatment effects from RCTs to defined target populations. Participants will receive SAS and R code to combine publicly available RCT and real-world data. Participants will gain an understanding of the theory underlying external validity. Using graphics and quantitative metrics, participants will evaluate the suitability of and compare effect estimates transported to various target populations.

This workshop requires an introductory level of epidemiology training and is relevant for all interested in expanding their epidemiological toolkit. This workshop may be of particular interest to those focused on causal inference methods, pharmacoepidemiology, and comparative effectiveness research.

Jan
11
Mon
An Introduction to R for Epidemiologists
Jan 11 @ 12:00 pm – 4:00 pm

Session Chair: Steve Mooney, University of Washington

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.

Jan
15
Fri
Scientific Manuscript Writing for Peer Review Journals: Communicating Results of Studies
Jan 15 @ 12:00 pm – 4:00 pm

Session Chair: Moyses Szklo, JHSPH

In this half-day workshop, participants will critically review a paper as initially submitted to the American Journal of Epidemiology, 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 regarding 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 and prepare a consolidated list of critical comments on the paper. Each group will designate a leader who will present the group’s review of the paper 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 accepted version of the paper.

Jan
22
Fri
Confounding control for estimating causal effects: Looking under the hood
Jan 22 @ 12:00 pm – 4:00 pm

Session Co-Chair: Nicolle Gatto, Pfizer Inc.
Session Co-Chair: Ulka Campbell, Pfizer Inc.

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 scenario of time-dependent confounding. Participants will learn how to apply these methods in SAS and R using an observational dataset with the primary goal of unpacking any “black boxes” to clarify the links among the causal effect of interest, the mechanics of these g-methods, and the programming code.