Randomized clinical trials (RCTs) are the gold standard to quantify the effectiveness and safety of medical interventions. Unfortunately, many clinical questions have not been answered yet by a RCT, and observational studies are the next best option to fill the gaps.
Most of the efforts in observational analyses are focused on adjusting for measured confounders. In this webinar we will go one step beyond and describe design strategies to avoid selection bias and improve efficiency in observational studies.
- How to map the identifiability conditions of causal effects in the design of a RCT, and in an observational study;
- To identify the problems that can arise when eligibility, start of follow-up and exposure assignment are not aligned; and
- How to improve the precision of the effect estimates when patients are eligible at multiple time points.