Applied Sensitivity Analyses in Pharmacoepidemiology Database Studies

It is widely recognized that sensitivity analyses of study design choices and analytic assumptions help to interpret the robustness of pharmacoepidemiology studies. To encourage increased use of well-described techniques, this workshop will provide an introduction and demonstrations of a range of sensitivity analyses typically applied in pharmacoepidemiology with hands-on exercises. Part 2 of the course is interactive and participants will be able to conduct their own sensitivity analyses during the course — bring your own computer!

Part 1: Introduction to principled epidemiology, using visual design and the application of sensitivity analysis from concept to interpretation. Introduction to the case-study.

Part 2: Sensitivity analyses of study design choices will introduce testing typical variations in study designs, including choice of comparator, defining discontinuation of medication use (i.e., allowable gap), and variations in exposure risk window length, covariate assessment window length, duration of minimum induction period, and follow-up model (as-treated analysis with censoring that accounts for treatment changes vs. ITT analysis). Quantitative confounding bias analysis will focus on testing the influence of external assumptions or outside data on our understanding of residual confounding in the specific study setting. All sensitivity analyses will be conducted with audience input based on the case study introduced in Part 1.
This 4-hour pre-conference course focuses on principles and concepts, not on mathematical details. The course faculty includes members from academia, industry and regulatory to teach a broad perspective on the uses and interpretation of sensitivity analyses.

Educational objectives
Upon completing this course, participants will be able to:
· Recognize the value and applicability of sensitivity analysis in pharmacoepidemiology research
· To discuss the findings of sensitivity analyses of study design choices and understand the impact of varying study design assumptions in practical examples
· To explain quantitative confounding bias analysis and understand their interpretation in specific examples
· To develop strategies on how to plan for and communicate results of sensitivity analyses from the perspective of various stakeholders

Target audience
· This course is designed for scientists, regulators, coverage decision makers conducting and interpreting pharmacoepidemiology research studies using healthcare databases
· This beginner-to-intermediate-level course assumes knowledge of basic pharmacoepidemiologic methods