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Activity Number:
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518
- Estimand, Causal Inference, and Other Statistical Considerations in Clinical Trials
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Type:
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Contributed
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Date/Time:
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Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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Abstract #323456
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Title:
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Bayesian Approach to Principal Stratum in a Clinical Setting
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Author(s):
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Dominique McDaniel* and Ahmad Hakeem Abdul Wahab and Run Zhuang and Arman Sabbaghi
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Companies:
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Drexel University and Janssen Pharmaceuticals and Purdue University and Purdue University
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Keywords:
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Estimands;
Causal Inference ;
Bayesian ;
Tripartite;
Clinical Trials;
ICH(E9)
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Abstract:
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Patients in clinical trials may discontinue their randomized study treatments due to intercurrent events such as adverse side-effects. Accordingly, clinical trials for drug approval must evaluate treatment effects while accounting for intermediate outcomes, to prevent biased inferences that could arise from confounding of latent variables. Existing statistical methodologies for clinical trials such as intention- to – treat typically ignore treatment discontinuation and focus on treatment assignment. In this research presentation, we will discuss principal strata that arise due to intercurrent events in randomized comparative trials. Further, we will introduce a Bayesian testing methodology that can account for the existence of principal strata in such clinical trials. The potential utility of this methodology will be illustrated on simulated clinical trials from a novel clinical trial with intercurrent events simulator (CITIES) that accounts for multiple intercurrent events under the potential outcomes framework. Lastly we will also demonstrate the utility on a simulated real clinical trial.
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Authors who are presenting talks have a * after their name.
