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Published on: September 20, 2019
Statistical methods for targeted clinical trials under enrichment design
1Division of Biometry, Graduate Institute of Agronomy, National Taiwan University, Taipei, and Division of Biostatistics and Bioinformatics, National Health Research Institutes, Zhunan, Taiwan. jpliu@ntu.edu.tw
Background/Purpose:
After completion of the Human Genome Project, disease targets at the molecular level can be identified. Treatment for these specific targets can be developed with the individualized treatment of patients becoming a reality. However, the accuracy of diagnostic devices for molecular targets is not perfect and statistical inference for treatment effects of the targeted therapy is biased. We developed statistical methods for an unbiased inference for the targeted therapy in patients who truly have the molecular targets.
Methods:
Under the enrichment design, for binary data, we propose using the expectation maximization (EM) algorithm with the bootstrap method, to incorporate the inaccuracy of the diagnostic device for detection of the molecular targets for inference of the treatment effects. A simulation study was conducted to empirically investigate the performance of the proposed estimation and testing procedures. A numerical example illustrates the application of the proposed method.
Results:
Simulation results demonstrated that the proposed estimation method was unbiased, with adequate precision, and the confidence interval provided satisfactory coverage probability. The proposed testing procedure adequately controlled the size with sufficient power. The numerical example showed that a statistically significant treatment effect could be obtained when the inaccuracy of the diagnostic device was taken into account.
Conclusion:
Our proposed estimation and testing procedures are adequate statistical methods for the inference of the treatment effect for patients who truly have the molecular targets.
Insights
New statistical methods provide unbiased treatment effect inference for targeted therapies, accounting for diagnostic inaccuracies in patients with molecular targets. This improves precision medicine outcomes.
Area of Science:
- Biostatistics
- Genomics
- Precision Medicine
Background:
- The Human Genome Project enables identification of molecular targets for disease treatment.
- Individualized treatment strategies are advancing, but diagnostic inaccuracies pose challenges.
- Current statistical inference for targeted therapies can be biased due to imperfect diagnostic devices.
Purpose of the Study:
- To develop statistical methods for unbiased inference of treatment effects.
- To address biased statistical inference in targeted therapy for patients with molecular targets.
- To account for diagnostic device inaccuracies in evaluating treatment efficacy.
Main Methods:
- Proposed expectation maximization (EM) algorithm with bootstrap for binary data under enrichment design.
- Incorporated diagnostic device inaccuracy for robust treatment effect inference.
- Conducted simulation studies and a numerical example to validate methods.
Main Results:
- The proposed estimation method demonstrated unbiasedness with adequate precision.
- Confidence intervals achieved satisfactory coverage probability.
- The testing procedure controlled statistical size and provided sufficient power, revealing significant treatment effects when diagnostic inaccuracy was considered.
Conclusions:
- The developed estimation and testing procedures offer adequate statistical methods.
- These methods enable reliable inference of treatment effects for patients with molecular targets.
- Accurate statistical inference is crucial for effective targeted therapy in precision medicine.
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