Understanding causal relationships in real-world data is challenging, particularly in observational studies, where confounding can introduce bias. If confounding is not properly accounted for, treatment effect estimates may be misleading, impacting decision-making.
Therefore, correctly modelling confounding is essential for valid causal inferences. Two popular approaches to achieve this are inverse probability of treatment weighting (IPTW) and the parametric g-formula. Both methods assume confounding has been adequately modelled. However, what if it is not?
Historically, this is listed as a limitation. In recent years, sensitivity analyses have become increasingly popular to better understand this assumption. This webinar will focus on using the Bayesian parametric g-formula as a sensitivity analysis for measured confounding. Prior information about confounders can be incorporated into the model. This allows for a better understanding of the range of the plausible effects. Various scenarios can also be considered including if the confounder is a weak confounder or strong confounder.
This approach offers a practical solution for researchers to ensure the validity of findings.
Learning Points:
- How Bayesian methods improve sensitivity analyses
- Incorporating prior knowledge about confounding
- Practical applications for real-world data research
Watch this webinar recording and explore how the Bayesian Parametric G-Formula can be used as a sensitivity analysis tool to strengthen causal inference and improve the validity of findings.
Complete the form below to access the recording and slides.
