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Activity Number:
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441
- Bayesian (and Other) Clinical Trials Designs
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #317928
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Title:
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Bayesian Logistic Regression with Covariates in Oncology Dose Escalation
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Author(s):
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Arnab Kumar Maity* and MARZIEH GOLMAKANI and LADA ALEKSANDROVNA MARKOVTSOVA
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Companies:
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Pfizer and PFIZER and PFIZER
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Keywords:
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BLRM;
EWOC;
Oncology;
Phase I;
MTD;
DLT
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Abstract:
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The traditional dose finding design guided by the Bayesian logistic regression model (BLRM) with escalation with overdose control (EWOC) models the probability of dose limiting toxicity (DLT) as a function of doses received by the trial participants. Then the probability of DLT is categorized into under dosing probability, target toxicity probability, and overdosing probability to provide statistical recommendation for dose escalation or dose de-escalation. When taking the final decision toward declaring the maximum tolerated dose (MTD) or the recommended phase II dose (RP2D) the trial investigators also consider the biomarkers measurements obtained during the study. However, these biomarkers are only studied in subjective manner and generally not included in the dose finding model. In this work, we consider the problem of including the biomarkers in the Bayesian logistic regression as covariates. We discuss the potential advantages and challenges and demonstrate a few avenues to overcome them using real data examples.
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Authors who are presenting talks have a * after their name.
