In the field of medicine, researchers often study how new treatments affect patients in real-world settings. However, these studies are prone to a problem called "confounding," which can make it difficult to determine if the treatment actually works or if the results are due to other factors. To address this issue, we propose a novel approach that combines the strengths of both randomized and observational studies. Our method detects unobserved confounding using a statistical test and estimates an asymptotically valid lower bound on the true confounding strength. We demonstrate the effectiveness of our approach through simulations and a real-world example, showing how it can help researchers make more accurate causal inferences in precision medicine.
Introduction
In the quest to develop new treatments for various diseases, clinical trials are crucial to evaluate their safety and efficacy. However, these trials often take place in a controlled environment, which may not reflect real-world scenarios. As a result, researchers face the challenge of confounding variables that can skew the results, making it hard to determine if the treatment is truly effective. To overcome this obstacle, we propose a strategy that combines the strengths of both randomized and observational studies.
Our Approach
Our method detects unobserved confounding using a statistical test and estimates an asymptotically valid lower bound on the true confounding strength. We developed a test to determine if the observed treatment effect is due to confounding or the treatment itself. If strong confounding is detected, we can identify relevant covariates to incorporate into the study design, reducing the impact of confounding. On the other hand, if small confounding is detected, we can continue with our analysis without making significant changes.
Advantages
Our approach has several advantages over existing methods. Firstly, it can detect unobserved confounding more accurately than traditional methods, which rely on assumptions about the confounding bias structure. Secondly, our method provides a lower bound on the true confounding strength, allowing researchers to take proactive measures to address confounding before conducting randomized trials. Finally, our approach can be applied to both observational and randomized studies, making it a versatile solution for precision medicine.
Real-World Example
To illustrate our method’s effectiveness, we applied it to a real-world example of a drug regulatory process. In this scenario, our lower bound allowed researchers to identify the presence of unobserved confounding and take proactive measures to address it before conducting randomized trials. By doing so, they could ensure that their analysis was more accurate and reliable.
Conclusion
In conclusion, we propose a novel approach that combines the strengths of both randomized and observational studies to address confounding in precision medicine. Our method detects unobserved confounding using a statistical test and estimates an asymptotically valid lower bound on the true confounding strength. By taking proactive measures to address confounding, researchers can make more accurate causal inferences and develop safer and more effective treatments for various diseases.
