Citations

Abstract Pancreatogenic diabetes mellitus is most commonly the result of chronic pancreatitis but can also occur secondary to pancreatic cancer. The early identification of pancreatogenic diabetes and distinction from the more prevalent type 2 diabetes are clinically significant; however, currently, there is no validated method to differentiate these diabetes subtypes. We describe a study, “Evaluation of a Mixed Meal Test for Diagnosis and Characterization of PancrEaTogEniC DiabeTes Secondary to Pancreatic Cancer and Chronic Pancreatitis: the DETECT study,” that seeks to address this knowledge gap. The DETECT study is a multicenter study that will examine differences in hormone and glucose excursions after a mixed meal test. The study will also create a biorepository that will be used to evaluate novel diagnostic biomarkers for differentiating these diabetes subtypes.

SummaryThe landscape of oncology drug development has recently changed with the emergence of molecularly targeted agents and immunotherapies. These new therapeutic agents appear more likely to induce multiple low or moderate grade toxicities rather than dose limiting toxicities. Various model-based dose finding designs and toxicity severity scoring systems have been proposed to account for toxicity grades, but they are difficult to implement because of the use of complicated dose–toxicity models and the requirement to refit the model at each decision of dose escalation and de-escalation. We propose a generalized Bayesian optimal interval design, gBOIN, that accommodates various existing toxicity grade scoring systems under a unified framework. As a model-assisted design, gBOIN derives its optimal decision rule on the basis of the exponential family of distributions but is carried out in a simple way as the algorithm-based design: its decision of dose escalation and de-escalation involves only a simple comparison of the sample mean of the end point with two prespecified dose escalation and de-escalation boundaries. No model fitting is needed. We show that gBOIN has the desirable finite property of coherence and a large sample property of consistency. Numerical studies show that gBOIN yields good performance that is comparable with or superior to that of some existing, more complicated model-based designs. A Web application for implementing gBOIN is freely available from http://www.trialdesign.org.

Abstract The primary objective of phase I oncology trials is to find the MTD. The 3+3 design is easy to implement but performs poorly in finding the MTD. A newer design, such as the modified toxicity probability interval (mTPI) design, provides better accuracy to identify the MTD but tends to overdose patients. We propose the keyboard design, an intuitive Bayesian design that conducts dose escalation and de-escalation based on whether the strongest key, defined as the dosing interval that most likely contains the current dose, is below or above the target dosing interval. The keyboard design can be implemented in a simple way, similar to the traditional 3+3 design, but provides more flexibility for choosing the target toxicity rate and cohort size. Our simulation studies demonstrate that compared with the 3+3 design, the keyboard design has favorable operating characteristics in terms of identifying the MTD. Compared with the mTPI design, the keyboard design is safer, with a substantially lower risk of treating patients at overly toxic doses, and has the better precision to identify the MTD, thereby providing a useful upgrade to the mTPI design. Software freely available at http://www.trialdesign.org facilitates the application of the keyboard design. Clin Cancer Res; 23(15); 3994–4003. ©2017 AACR. See related commentary by Paoletti et al., p. 3977