Summer 2020 dates: Live-stream, online training August 12-14, 2020; 10:00am - 5:00pm EDT
Mediation analysis is an emerging field in causal inference relevant for comparative effectiveness research, evaluating and improving policy recommendations, and explaining biological mechanisms. Training in the potential outcomes framework for causal inference is important to understand the assumptions required for valid mediation analyses. This course will equip participants with foundational concepts and cutting edge statistical tools to investigate mediating mechanisms.
This three-day intensive course will cover some of the recent developments in causal mediation analysis and provide practical tools to implement these techniques and assess the mechanisms and pathways by which causal effects operate. Led by a team of experts in causal mediation techniques at Columbia University, this course will integrate lectures and discussion with hands-on computer lab sessions using R and SAS. The course will cover the relationship between traditional methods for mediation in environmental health, epidemiology, and the social sciences and new methods in causal inference using a wide variety of examples to illustrate the techniques and approaches. We will discuss 1) when the standard approaches to mediation analysis are valid for dichotomous, continuous, and time-to-event outcomes, 2) alternative mediation analysis techniques when the standard approaches will not work, using ideas from causal inference and natural direct and indirect effects 3) the no-unmeasured confounding assumptions needed to identify these effects, and 4) how regression approaches for mediation analysis can be extended in the presence of multiple mediators.
By the end of the workshop, participants will be familiar with the following topics:
Understand when traditional methods for mediation fail
Articulate concepts about mediation under the counterfactual framework and assumptions for identification
Formulate and apply regression approaches for mediation for single and multiple mediators
Develop facility with use of software for mediation and interpretation of software output
Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.
PREREQUISITES AND REQUIREMENTS
Each participant must be familiar with linear and logistic regression.
Each participant must have experience with programming in SAS and/or R.
Although the instructors will provide an overview of the fundamentals of causal inference (potential outcomes, directed acyclic graphs, and marginal structural models), we invite the participants to read chapters 1-7, 11, and 12 of Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC (free).
Each participant is required to bring a personal laptop with R/RStudio installed prior to the first day of the workshop, as all lab sessions will be done on your personal laptop. R is available for free download and installation on Mac, PC, and Linux devices. You will receive a SAS/Stata license for the duration of the training.