Journal of biopharmaceutical statistics
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Phase I designs traditionally use the dose-limiting toxicity (DLT), a binary endpoint from the first treatment cycle, to identify the maximum-tolerated dose (MTD) assuming a monotonically increasing relationship between dose and efficacy. In this article, we establish a general framework for a multi-stage adaptive design where we jointly model a continuous efficacy outcome and continuous/quasi-continuous toxicity endpoints from multiple treatment cycles. The normalized Total Toxicity Profile (nTTP) is used as an illustration for quasi-continuous toxicity endpoints, and we replace DLT with nTTP to take into account multiple grades and types of toxicities. ⋯ Simulations showed that the design had a high probability of making the correct dose selection and good overdose control across various dose-efficacy and dose-toxicity scenarios. In addition, the proposed design allows for early termination when all doses are too toxic. To our best knowledge, the proposed dual-endpoint dose-finding design is the first such study to incorporate multiple cycles of toxicities and a continuous efficacy outcome.
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Precision medicine has been a hot topic in drug development over the last decade. Biomarkers have been proven useful for understanding the disease progression and treatment response in precision medicine development. ⋯ In this article, we discuss the technologies and statistical issues that are related to omics biomarker discovery. We also provide an overview of the current development of biomarker-enabled cancer clinical trial designs.
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Five algorithms are described for imputing partially observed recurrent events modeled by a negative binomial process, or more generally by a mixed Poisson process when the mean function for the recurrent events is continuous over time. We also discuss how to perform the imputation when the mean function of the event process has jump discontinuities. ⋯ These imputation algorithms are potentially very useful in the implementation of pattern mixture models, which have been popularly used as sensitivity analysis under the non-ignorability assumption in clinical trials. A chronic granulomatous disease trial is analyzed for illustrative purposes.
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In Phase I/II trials for a combination therapy of two agents, we ideally want to explore as many dose combinations as possible with limited sample size in Phase I and to reduce the number of untried dose combinations before moving to Phase II. Efficient collection of toxicity data in Phase I would eventually improve the accuracy of optimal dose combination identification in Phase II. In this paper, we develop a novel dose-finding method based on efficacy and toxicity outcomes for two-agent combination Phase I/II trials. ⋯ Upon completion of this zone-finding stage, we allocate the next patient to the dose combination determined by adaptive randomization of the admissible toxicity and efficacy dose combinations in Phase II. Simulation studies demonstrated the utility of the proposed zone-finding stage and proved that the operating characteristic of the proposed method was no worse than the existing method. The sensitivity of the proposed method, as well as the operating characteristic of this method when the efficacy outcome is delayed, was also examined.