Choosing Patients for Randomized Trials Based on Risk Group
Author Information
Author(s): Andrew J Vickers, Barry S Kramer, Stuart G Baker
Primary Institution: Memorial Sloan-Kettering Cancer Center
Hypothesis
Can a statistical approach improve the selection of patients for randomized trials by determining the optimal risk group?
Conclusion
A simple statistical method can inform the choice of risk group in randomized trials, leading to better trial designs.
Supporting Evidence
- The method provides a clear clinical interpretation that can aid trial design.
- Using a high-risk group can lead to better outcomes compared to treating all patients.
- The approach allows for the comparison of different definitions of high risk.
Takeaway
This study shows that we can use math to decide if we should treat everyone or just those at high risk in medical trials.
Methodology
The study uses a statistical approach to compare the outcomes of treating all patients versus only high-risk patients in randomized trials.
Potential Biases
Subjective judgment is involved in determining the number needed to treat (NNTt) and predicting relative risk.
Limitations
The method assumes that the relative risk for intervention is constant across different risk groups, which may not always be true.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website