Avoid Selection Bias and Improve Efficiency in Observational Studies

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Webinar: Beyond Controlling for Confounding

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Going One Step Further in Observational Studies: Design Strategies

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 an RCT, and observational studies are the next best option to fill the gaps.  For example, if we suspect a drug has a rare toxicity after years of being marketed, we may resort to analyzing real-world data in a post-authorization safety study (PASS) by emulating a target trial.

Most of the effort in observational study design is 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

You’ll learn

  • How to map the identifiability conditions of causal effects in the design of an 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
  • How to improve the precision of the effect estimates when patients are eligible at multiple time points