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 using healthcare database studies. 

Part 1 provides a brief introduction to principled pharmacoepidemiology with healthcare databases, and introduces the logic of sensitivity analyses during the study design phase and post-hoc quantitative bias analyses of effect estimates. We explain the value of study design diagrams and directed acyclic graphs in this process. We conclude by introducing a case study to be used in Parts 2 and 3. 

Part 2 will use a case study to work through several sensitivity analyses of study design choices by introducing 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). All sensitivity analyses will be conducted with audience input based on the case study introduced in Part 1. 

Part 3: Focusing on confounding we use DAGs to explain the concept of negative/positive control outcomes and discuss examples. This is followed by quantitative confounding bias analysis 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: a. Recognize the value and applicability of sensitivity analysis in pharmacoepidemiology research b. To discuss the findings of sensitivity analyses of study design choices and understand the impact of varying study design assumptions in practical examples c. To explain quantitative confounding bias analysis and understand their interpretation in specific examples d. 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