Predicting outcomes of phase III oncology trials with Bayesian mediation modeling of tumor response

DOI10.1002/SIM.9268zbMATH Open1545.6269MaRDI QIDQ6622284

Xun Jiang, Jie Zhou, Peng Wei, Hong Amy Xia, Brian P. Hobbs

Publication date: 22 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

cancerBayesian predictionphase III clinical trialsclinical trial simulationmediation modeling

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Analysis of survival data with dependent censoring. Copula-based approaches
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• Proportional odds regression and sieve maximum likelihood estimation
• Maximum Likelihood Estimation in the Proportional Odds Model
• Rank-based inference for the accelerated failure time model
• A Bayesian Semiparametric Accelerated Failure Time Model
• Interim analysis incorporating short‐ and long‐term binary endpoints
• Evaluating futility of a binary clinical endpoint using early read-outs

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