Premium
Bayesian group sequential clinical trial design using total toxicity burden and progression‐free survival
Author(s) -
Hobbs Brian P.,
Thall Peter F.,
Lin Steven H.
Publication year - 2016
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12117
Subject(s) - toxicity , bayesian probability , clinical trial , progression free survival , oncology , medicine , overall survival , computer science , artificial intelligence
Summary Delivering radiation to eradicate a solid tumour while minimizing damage to nearby critical organs remains a challenge. For oesophageal cancer, radiation therapy may damage the heart or lungs, and several qualitatively different, possibly recurrent toxicities that are associated with chemoradiation or surgery may occur, each at two or more possible grades. We describe a Bayesian group sequential clinical trial design, based on total toxicity burden (TTB) and the duration of progression‐free survival, for comparing two radiation therapy modalities for oesophageal cancer. Each patient's toxicities are modelled 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 who are 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 design proposed is shown to provide both larger power and smaller mean sample size when compared with a conventional bivariate group sequential design.