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Impervious to Randomness: Confounding and Selection Biases in Randomized Clinical Trials
1Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Cancer Investigation
|September 13, 2021
Summary
Randomized clinical trials eliminate treatment assignment bias. However, mediators of treatment effects, like biomarkers, require careful consideration in trial design and analysis for patient-specific insights.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Translational Oncology
Background:
- Randomized clinical trials (RCTs) utilize random allocation to eliminate confounding biases in treatment assignment.
- Confounders affecting treatment effect mediators remain unaddressed by randomization and necessitate specific analytical strategies.
- Biomarkers predictive of targeted therapy response in oncology exemplify such mediators, influencing treatment efficacy.
Purpose of the Study:
- To highlight the limitations of randomization concerning treatment effect mediators.
- To emphasize the importance of considering these mediators in clinical trial design and statistical modeling.
- To explore the integration of experimental and observational data for enhanced patient-specific inferences.
Main Methods:
- Review of randomization principles in clinical trials.
- Identification of mediators influenced by confounders.
- Conceptual framework for fusing experimental (RCT) and observational data.
Main Results:
- Randomization effectively controls for treatment assignment bias.
- Confounders influencing treatment mediators are not controlled by randomization.
- Observational data provide prognostic insights into outcome heterogeneity.
- Integrating RCT and observational data enables patient-specific treatment inferences.
Conclusions:
- Clinical trial design must account for confounders impacting treatment mediators.
- Biomarker-guided patient selection is crucial for targeted therapies.
- Combining experimental and observational data offers a powerful approach for personalized medicine and understanding treatment effects.
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