We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). In our proposed local‐MEM framework, information borrowing is only allowed to occur locally, that is, among baskets with similar response rate and the amount of information borrowing is determined by the level of similarity in response rate, whereas baskets not considered similar are not allowed to share information. We construct a two‐stage design for phase II basket trials using the proposed strategy. The proposed method is compared to competing Bayesian methods and Simon's two‐stage design in a variety of simulation scenarios. We demonstrate the proposed method is able to maintain the family‐wise type I error rate at a reasonable level and has desirable basket‐wise power compared to Simon's two‐stage design. In addition, our method is computationally efficient compared to existing Bayesian methods in that the posterior profiles of interest can be derived explicitly without the need for sampling algorithms. R scripts to implement the proposed method are available at
https://github.com/yilinyl/Bayesian-localMEM.
For any given city, on any calendar day, there will be record high and low temperatures. Which record occurred earlier? If there is a trend towards warming then, intuitively, there should be a preponderance of record highs occurring more recently than the record lows for each of the 365 calendar days. We are interested in modeling the joint distribution of appearances of the extremes but not these values themselves. We develop a bivariate discrete distribution modeling the joint indices of maximum and minimum in a sequence of independent random variables sampled from different distributions. We assume these distributions share a proportional hazard rate and develop regression methods for these paired values. This approach has reasonable power to detect a small mean change over a decade. Using readily available public data, we examine the daily calendar extreme values of five US cities for the decade 2011–2020. We develop linear regression models for these data, describe models to account for calendar‐date dependence, and use diagnostic measures to detect remarkable observations.
Purpose: Disease progression during or after anti-PD-1-based treatment is common in advanced melanoma. Sotigalimab is a CD40 agonist antibody with unique epitope specificity and Fc-receptor binding profile optimized for activation of CD40-expressing antigen presenting cells. Preclinical data indicated that CD40 agonists combined with anti-PD1 could overcome resistance to anti-PD-1. Methods: We conducted a multi-center, open-label, phase II trial to evaluate the combination of sotigalimab 0.3mg/kg and nivolumab 360mg q3wk in patients with advanced melanoma following confirmed disease progression on anti-PD-1. The primary objective was to determine the objective response rate (ORR). Results: Thirty-eight subjects were enrolled and evaluable for safety. Thirty-three were evaluable for activity. Five confirmed partial responses (PR) were observed for an ORR of 15%. Two PRs are ongoing at 45.9+ and 26+ months while the other 3 responders relapsed at 41.1, 18.7, and 18.4 months. Median duration of response was at least 26 months. Two additional patients had stable disease for > 6 months. Thirty-four patients (89%) experienced at least one adverse event (AE) and 13% experienced a grade 3 AE related to sotigalimab. The most common AEs were pyrexia, chills, nausea, fatigue, pruritus, elevated liver function, rash, vomiting, headache, arthralgia, asthenia, myalgia, and diarrhea. There were no treatment-related SAEs, deaths, or discontinuation of sotigalimab due to AEs. Conclusions: Sotigalimab plus nivolumab had a favorable safety profile consistent with the toxicity profiles of each agent. The combination resulted in durable and prolonged responses in a subset of patients with anti-PD-1-resistant melanoma, warranting further evaluation in this setting.
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