Planning a Bayesian early‐phase phase I/II study for human vaccines in HER2 carcinomas

Abstract

Recent innovative statistical approaches for phase I/II clinical trials allow one to jointly model the toxicity and efficacy of a new treatment, taking into account the information gathered during the trial. Prior probabilities are then updated with interim data and thus predictive probabilities become more accurate as the trial progresses. In this study, prior distribution elicited from a physician's opinion on the available dose levels planned for a vaccination dose-finding trial, with human DNA in patients with HER2-positive tumours in terms of toxicity and therapeutic response is presented and discussed. A simulation study was conducted in order to quantify the impact of the choice of prior on study results, i.e. the recommended dose level at the end of the trial.

Bayesian sample size determination for phase IIA clinical trials using historical data and semi‐parametric prior's elicitation

“…In this manuscript, we exploit the results on response probability of DNA anti‐HER2 vaccine to build the design prior, and we use historical data, in terms of summary measures from phase I clinical trials identified in a systematic review, to derive the analysis prior.…”

“…Probabilities of efficacy elicited from 13 translational researchers and physicians for the phase I/II trial on DNA vaccination in HER2 carcinomas form the design prior. Details of the elicitation process are given in Zohar et al By exploiting the same methodology used for the analysis prior, ψ = 0.38 (estimated best fit) gives the best fit design prior (Figure 3), whereas ψ = 4 gives the flat design prior (Figure 3).…”

“…When designing a single‐arm phase IIA trial in the frequentist framework, not always a consensus about the expected or desired response rate ( θ 1 ) can be achieved by agreeing on a narrow range of potential values. This is apparent in the following case study on DNA anti‐HER2 vaccine developed for the treatment of HER2 over‐expressing breast cancer, which has been shown to increase tumor response . During the design of a phase I/II dose‐finding trial, 13 translational researchers and physicians involved in antitumor vaccination were asked for their prior opinion about the response rate of the DNA anti‐HER2 vaccine at the optimal dose, Figure A.…”

An adaptive power prior for sequential clinical trials – Application to bridging studies

“…Recently, methods were proposed that allow using external information into the dose-finding design, where either external data were used for choosing the skeleton (working model) of the design or for calibrating the prior of the dose-toxicity relationship parameter(s). 16,17 Liu et al proposed using a Bayesian model averaging (BMA) dose-finding method in which the estimated probabilities of toxicity at the end of the previous trial are used to build three different skeletons, that will be averaged during the present trial. 18 Takeda and Morita defined a "historical-to-current" (H-C) parameter representing the degree of borrowing based on a retrospective analysis of previous trial data.…”

A Bayesian design for phase I cancer therapeutic vaccine trials

“…For example, randomized phase I study designs have been proposed for optimizing the dose level or the schedule of therapeutic cancer vaccines . In a nonrandomized setting, Zohar et al proposed a Bayesian “up‐and‐down” phase I design for a cancer vaccine that makes dose level escalation decisions after each patient …”