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The phase II basket trial in oncology is a novel design that enables the simultaneous assessment of treatment effects of one anti‐cancer targeted agent in multiple cancer types. Biomarkers could potentially associate with the clinical outcomes and re‐define clinically meaningful treatment effects. It is therefore natural to develop a biomarker‐based basket design to allow the prospective enrichment of the trials with the adaptive selection of the biomarker‐positive (BM+) subjects who are most sensitive to the experimental treatment. We propose a two‐stage phase II adaptive biomarker basket (ABB) design based on a potential predictive biomarker measured on a continuous scale. At Stage 1, the design incorporates a biomarker cutoff estimation procedure via a hierarchical Bayesian model with biomarker as a covariate (HBMbc). At Stage 2, the design enrolls only BM+ subjects, defined as those with the biomarker values exceeding the biomarker cutoff within each cancer type, and subsequently assesses the early efficacy and/or futility stopping through the pre‐defined interim analyses. At the end of the trial, the response rate of all BM+ subjects for each cancer type can guide drug development, while the data from all subjects can be used to further model the relationship between the biomarker value and the clinical outcome for potential future research. The extensive simulation studies show that the ABB design could produce a good estimate of the biomarker cutoff to select BM+ subjects with high accuracy and could outperform the existing phase II basket biomarker cutoff design under various scenarios. [ABSTRACT FROM AUTHOR]

The new therapeutic agents, such as molecular targeted agents and immuno‐oncology therapies, appear more likely to induce multiple toxicities at different grades than dose‐limiting toxicities defined in traditional dose‐finding trials. In addition, it is often challenging to make adaptive decisions on dose escalation and de‐escalation on time because of the fast accrual rate and/or the late‐onset toxicity outcomes, causing the potential suspension of the enrollment and the delay of the trials. To address these issues, we propose a time‐to‐event Bayesian optimal interval design to accelerate the dose‐finding process utilizing toxicity grades based on both cumulative and pending toxicity outcomes. The proposed design, named "TITE‐gBOIN" design, is a nonparametric and model‐assisted design and has the virtues of robustness, simplicity and straightforward to implement in actual oncology dose‐finding trials. A simulation study shows that the TITE‐gBOIN design has a higher probability of selecting the MTDs correctly and allocating more patients to the MTDs across various realistic settings while reducing the trial duration significantly, therefore can accelerate early‐stage dose‐finding trials. [ABSTRACT FROM AUTHOR]

The basket trial in oncology is a novel clinical trial design that enables the simultaneous assessment of one treatment in multiple cancer types. In addition to the usual basket classifier of the cancer types, many recent basket trials further contain other classifiers like biomarkers that potentially affect the clinical outcomes. In other words, the treatment effects in those baskets are often categorized by not only the cancer types but also the levels of other classifiers. Therefore, the assumption of exchangeability is often violated when some baskets are more sensitive to the targeted treatment, whereas others are less. In this article, we propose a constrained hierarchical Bayesian model for latent subgroups (CHBM‐LS) to deal with potential heterogeneity of treatment effects due to both the cancer type (first classifier) and another classifier (second classifier) in basket trials. Different baskets defined by multiple cancer types and multiple levels of the second classifier are aggregated into subgroups using a latent subgroup modeling approach. Within each latent subgroup, the treatment effects are similar and approximately exchangeable to borrow information. The CHBM‐LS approach evaluates the treatment effect for each basket while allowing adaptive information borrowing across the baskets by identifying latent subgroups. The simulation study shows that the CHBM‐LS approach outperforms other approaches with higher statistical power and better‐controlled type I error rates under various scenarios with heterogeneous treatment effects across baskets. [ABSTRACT FROM AUTHOR]

In randomized controlled trials with delayed treatment effect, there is a delay period before the experimental therapy starts to exhibit a beneficial effect. The phenomenon of delayed treatment effect is often observed in the emerging and important field of immuno-oncology. It is important to estimate the duration of delay as this information helps in characterizing the pattern of comparative treatment effect, understanding the mechanism of action of the experimental therapy, and forming optimal treatment strategies. For a fixed delay time, we propose a maximum likelihood estimator and evaluate its asymptotic properties via simulation. We further evaluate two functions that link the pre- and postdelay hazard ratios to the average hazard ratio given a fixed delay time. For the case of random delay time, where the delay time may vary from patient to patient, we propose a semiparametric joint survival model for delay time and event time to estimate the mean delay time and the postdelay hazard ratio, assuming a Beta distribution for the delay time. We describe an extension of the model to estimate subgroup-specific mean delay times. Simulation study and application to data from a clinical trial in colon cancer patients demonstrate the robustness of the proposed model. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Panitumumab-FOLFOX4 Treatment and RAS Mutations in Colorectal Cancer," by J. Y. Douillard and colleagues in the September 12, 2013 issue.