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Activity Number:
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540
- SPEED: Clinical Trial Design, Longitudinal Analysis, and Other Topics in Biopharmaceutical Statistics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 11:35 AM to 12:20 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #332874
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Title:
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Exposure-Response Analysis with Random Forest
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Author(s):
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Zifang Guo* and Thomas Jemielita and John Kang
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Companies:
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Merck and Merck & Co. and Merck
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Keywords:
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exposure-response analysis; drug development; logistic regression; machine learning; random forest
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Abstract:
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Over the past decade, exposure-response analysis has become an integral part of clinical drug development and regulatory decision-making. The current practice of exposure-response analysis typically relies on parametric modeling and involves step-wise procedures consisting of structural model selection, covariate selection, model fitting and model prediction. However, this current practice is subject to multiple issues such as model mis-specification and error propagation. In this presentation, we will discuss the application of random forest in exposure-response analysis along with its challenges and solutions. A new method utilizing both random forest and parametric modeling is proposed. Simulation results comparing the performance of the proposed method with existing approach will be presented.
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Authors who are presenting talks have a * after their name.
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