Observational studies are non-interventional empirical investigations of causal effects and are playing an increasingly vital role in healthcare decision making in the era of data science. The study design is particularly important in planning observational studies due to the lack of randomization. Aspects of design include defining the objectives and context under investigation, collecting the right data, and choosing suitable strategies to remove bias from measured and unmeasured confounders. Statistical analysis should also align with the design.
This module covers key concepts and useful methods for designing and analyzing observational studies. The first part of the module will focus on matching and weighting methods for cohort and case-control studies for causal inference. Specific topics include basic tools of matching and weighting, randomization inference, and sensitivity analysis. The second part of the module will focus on methods to address unmeasured confounding via causal exclusion. Specific topics include instrumental variables, negative controls, and difference-in-differences. Participants will also gain practical experience by applying these methods to real datasets using R.
Target audiences for this module are:
1. clinical researchers who need to use observational data to generate evidence of causality;
2. biostatisticians who are interested in understanding how causal inference can be reliably made in practice.
Background in statistical inference and some knowledge of R are recommended.
Instructors: Ting Ye, Richard Guo
Teaching assistant: Yilin Song
Time: July 8-10, 2024
You should have access to the Slack channel for this module. If not, please contact us.
Lectures will be delivered via Zoom and be recorded. The recordings will be posted on the course website when they are available. Practical sessions will not be recorded.
Day 1 Lecture 1: Causal inference in randomized controlled trials (lecture notes and practice, recording)
Day 1 Lecture 2: Causal inference in observational studies and matching (lecture notes and practice)
Day 2 Lecture 3: G-computation, IPW, and AIPW (lecture notes and practice)
Day 2 Lecture 4: Instrumental variables and Mendelian randomization (lecture notes and practice)
Day 3 Lecture 5: Negative controls; Difference-in-Differences (lecture notes and practice)
Day 3 Lecture 6: Time-varying exposures (lecture notes)
Before the module starts, please ensure that you have installed the latest version of R. We also recommend you to use an integrated development environment like RStudio.
Please ensure the following R packages are installed before the module starts, e.g., using install.packages("tidyverse"): tidyverse, MASS, RobinCar