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Bayesian Group Sequential Clinical Trial Design using Total Toxicity Burden and Progression-Free Survival.

ABSTRACT: Delivering radiation to eradicate a solid tumor while minimizing damage to nearby critical organs remains a challenge. For esophageal cancer, radiation therapy may damage the heart or lungs, and several qualitatively different, possibly recurrent toxicities associated with chemoradiation or surgery may occur, each at two or more possible grades. In this article, we describe a Bayesian group sequential clinical trial design, based on total toxicity burden (TTB) and progression-free survival duration, for comparing two radiation therapy modalities for esophageal cancer. Each patient's toxicities are modeled as a multivariate doubly stochastic Poisson point process, with marks identifying toxicity grades. Each grade of each type of toxicity is assigned a severity weight, elicited from clinical oncologists familiar with the disease and treatments. TTB is defined as a severity-weighted sum over the different toxicities that may occur up to 12 months from the start of treatment. Latent frailties are used to formulate a multivariate model for all outcomes. Group sequential decision rules are based on posterior mean TTB and progression-free survival time. The proposed design is shown to provide both larger power and smaller mean sample size when compared to a conventional bivariate group sequential design.

SUBMITTER: Hobbs BP

PROVIDER: S-EPMC4809549 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Bayesian Group Sequential Clinical Trial Design using Total Toxicity Burden and Progression-Free Survival.

Hobbs Brian P BP Thall Peter F PF Lin Steven H SH

Journal of the Royal Statistical Society. Series C, Applied statistics 20151026 2

Delivering radiation to eradicate a solid tumor while minimizing damage to nearby critical organs remains a challenge. For esophageal cancer, radiation therapy may damage the heart or lungs, and several qualitatively different, possibly recurrent toxicities associated with chemoradiation or surgery may occur, each at two or more possible grades. In this article, we describe a Bayesian group sequential clinical trial design, based on total toxicity burden (TTB) and progression-free survival durat ...[more]

PMID: 27034510

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Project description:Advanced head and neck squamous cell carcinoma (HNSCC) is associated with a poor prognosis, and biomarkers that predict response to treatment are highly desirable. The primary aim was to predict progression-free survival (PFS) with a multivariate risk prediction model. Experimental covariates were derived from blood samples of 56 HNSCC patients which were prospectively obtained within a Phase 2 clinical trial (NCT02633800) at baseline and after the first treatment cycle of combined platinum-based chemotherapy with cetuximab treatment. Clinical and experimental covariates were selected by Bayesian multivariate regression to form risk scores to predict PFS. A 'baseline' and a 'combined' risk prediction model were generated, each of which featuring clinical and experimental covariates. The baseline risk signature has three covariates and was strongly driven by baseline percentage of CD33+CD14+HLADRhigh monocytes. The combined signature has six covariates, also featuring baseline CD33+CD14+HLADRhigh monocytes but is strongly driven by on-treatment relative change of CD8+ central memory T cells percentages. The combined model has a higher predictive power than the baseline model and was successfully validated to predict therapeutic response in an independent cohort of nine patients from an additional Phase 2 trial (NCT03494322) assessing the addition of avelumab to cetuximab treatment in HNSCC. We identified tissue counterparts for the immune cells driving the models, using imaging mass cytometry, that specifically colocalized at the tissue level and correlated with outcome. This immune-based combined multimodality signature, obtained through longitudinal peripheral blood monitoring and validated in an independent cohort, presents a novel means of predicting response early on during the treatment course. Daiichi Sankyo Inc, Cancer Research UK, EU IMI2 IMMUCAN, UK Medical Research Council, European Research Council (335326), Merck Serono. Cancer Research Institute, National Institute for Health Research, Guy's and St Thomas' NHS Foundation Trust and The Institute of Cancer Research. NCT02633800.

Dataset Information

Bayesian Group Sequential Clinical Trial Design using Total Toxicity Burden and Progression-Free Survival.

Publications

Bayesian Group Sequential Clinical Trial Design using Total Toxicity Burden and Progression-Free Survival.

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