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For novel immuno‐oncology therapies, the primary purpose of a dose‐finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose–outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate‐grade toxicities rather than dose‐limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose‐finding to determine true OD for developing novel immuno‐oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN‐ETI design, is model‐assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN‐ETI are compared with other dose‐finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN‐ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings. [ABSTRACT FROM AUTHOR]

With the emergence of molecular targeted agents and immunotherapies in anti-cancer treatment, a concept of optimal biological dose (OBD), accounting for efficacy and toxicity in the framework of dose-finding, has been widely introduced into phase I oncology clinical trials. Various model-assisted designs with dose-escalation rules based jointly on toxicity and efficacy are now available to establish the OBD, where the OBD is generally selected at the end of the trial using all toxicity and efficacy data obtained from the entire cohort. Several measures to select the OBD and multiple methods to estimate the efficacy probability have been developed for the OBD selection, leading to many options in practice; however, their comparative performance is still uncertain, and practitioners need to take special care of which approaches would be the best for their applications. Therefore, we conducted a comprehensive simulation study to demonstrate the operating characteristics of the OBD selection approaches. The simulation study revealed key features of utility functions measuring the toxicity-efficacy trade-off and suggested that the measure used to select the OBD could vary depending on the choice of the dose-escalation procedure. Modelling the efficacy probability might lead to limited gains in OBD selection. [ABSTRACT FROM AUTHOR]

One of the primary objectives of an oncology dose‐finding trial for novel therapies, such as molecular‐targeted agents and immune‐oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low or moderate‐grade toxicities than dose‐limiting toxicities. Besides, for efficacy, evaluating the overall response and long‐term stable disease in solid tumors and considering the difference between complete remission and partial remission in lymphoma are preferable. It is also essential to accelerate early‐stage trials to shorten the entire period of drug development. However, it is often challenging to make real‐time adaptive decisions due to late‐onset outcomes, fast accrual rates, and differences in outcome evaluation periods for efficacy and toxicity. To solve the issues, we propose a time‐to‐event generalized Bayesian optimal interval design to accelerate dose finding, accounting for efficacy and toxicity grades. The new design named "TITE‐gBOIN‐ET" design is model‐assisted and straightforward to implement in actual oncology dose‐finding trials. Simulation studies show that the TITE‐gBOIN‐ET design significantly shortens the trial duration compared with the designs without sequential enrollment while having comparable or higher performance in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings. [ABSTRACT FROM AUTHOR]

When some trials provide individual patient data (IPD) and the others provide only aggregate data (AD), meta-analysis methods for combining IPD and AD are required. We propose a method that reconstructs the missing IPD for AD trials by a Bayesian sampling procedure and then applies an IPD meta-analysis model to the mixture of simulated IPD and collected IPD. The method is applicable when a treatment effect can be assumed fixed across trials. We focus on situations of a single continuous outcome and covariate and aim to estimate treatment-covariate interactions separated into within-trial and across-trial effect. An illustration with hypertension data which has similar mean covariates across trials indicates that the method substantially reduces mean square error of the pooled within-trial interaction estimate in comparison with existing approaches. A simulation study supposing there exists one IPD trial and nine AD trials suggests that the method has suitable type I error rate and approximately zero bias as long as the available IPD contains at least 10% of total patients, where the average gain in mean square error is up to about 40%. However, the method is currently restricted by the fixed effect assumption, and extension to random effects to allow heterogeneity is required.