Early-phase oncology trials often require protocol-ready interim rules for deciding whether an experimental regimen shows sufficient activity relative to prespecified clinical benchmarks. Existing Bayesian optimal phase II designs provide calibrated count boundaries for such decisions, but evidence is quantified through posterior probabilities and sample-size-dependent cutoff functions. We propose calibrated e-value monitoring (CEVAM), a likelihood-ratio evidence framework that defines monitoring evidence directly relative to prespecified clinical benchmarks, without requiring a Bayesian analysis prior or sample-size-dependent posterior-probability cutoff functions. For a binary efficacy endpoint, CEVAM constructs efficacy and reverse-futility e-processes targeted to clinically meaningful benchmark rates and converts them into monotone response-count boundaries. We distinguish a fixed-threshold e-process version, which has an anytime-valid interpretation under optional monitoring, from planned-look calibrated versions designed for conventional finite-look phase II trials. In simulations based on binary benchmark settings used in existing posterior-cutoff phase II designs, the proposed tuned planned-look rule controlled the nominal type I error and achieved the smallest expected sample size across all evaluated settings while retaining similar rejection probabilities. In an application to an actual phase II breast cancer trial, CEVAM classified the reported pathological complete response result as sufficient evidence for early success stopping. Extensions to categorical and multicomponent endpoints are provided in the Supplementary Material. CEVAM offers an analysis-prior-free, protocol-ready likelihood-ratio evidence scale for binary benchmark monitoring in early-phase oncology trials.
With the rapid development of new anti-cancer agents which are cytostatic, new endpoints are needed to better measure treatment efficacy in phase II trials. For this purpose, Von Hoff (1998) proposed the growth modulation index (GMI), i.e. the ratio between times to progression or progression-free survival times in two successive treatment lines. An essential task in studies using GMI as an endpoint is to estimate the distribution of GMI. Traditional methods for survival data have been used for estimating the GMI distribution because censoring is common for GMI data. However, we point out that…
U-DESPE: a Bayesian Utility-based methodology for dosing regimen optimization in early-phase oncology trials based on Dose-Exposure, Safety, Pharmacodynamics, Efficacy
Leveraging preclinical animal data for a phase I first-in-man trial is appealing yet challenging. A prior based on animal data may place large probability mass on values of the dose-toxicity model parameter(s), which appear infeasible in light of data accrued from the ongoing phase I clinical trial. In this paper, we seek to use animal data to improve decision making in a model-based dose-escalation procedure for phase I oncology trials. Specifically, animal data are incorporated via a robust mixture prior for the parameters of the dose-toxicity relationship. This prior changes dynamically as …
In Oncology, trials evaluating drug combinations are becoming more common. While combination therapies bring the potential for greater efficacy, they also create unique challenges for ensuring drug safety. In Phase-I dose escalation trials of drug combinations, model-based approaches enable efficient use of information gathered, but the models need to account for trial complexities: appropriate modeling of interactions becomes increasingly important with growing numbers of drugs being tested simultaneously in a given trial. In principle, we can use data from multiple arms testing varying combi…
Learning from Literature: Integrating LLMs and Bayesian Hierarchical Modeling for Oncology Trial Design
Yale Cancer Center, Yale School of Medicine（耶鲁癌症中心，耶鲁医学院）；Pfizer Inc（辉瑞公司）；Public Health Sciences Division, Fred Hutchinson Cancer Center（弗雷德·哈钦森癌症中心公共卫生科学部）
Research in oncology has changed the focus from histological properties of tumors in a specific organ to a specific genomic aberration potentially shared by multiple cancer types. This motivates the basket trial, which assesses the efficacy of treatment simultaneously on multiple cancer types that have a common aberration. Although the assumption of homogeneous treatment effects seems reasonable given the shared aberration, in reality, the treatment effect may vary by cancer type, and potentially only a subgroup of the cancer types respond to the treatment. Various approaches have been propose…
PKComb-BOIN12: A Pharmacokinetically Guided Bayesian Design for Dose Optimisation in Oncology Drug-Combination Trials
Global Statistical Sciences, Eli Lilly and Company（礼来公司全球统计科学部）；Department of Biostatistics, The University of Texas MD Anderson Cancer Center（德克萨斯大学安德森癌症中心生物统计学系）；Population Health Sciences Institute, Newcastle University（纽卡斯尔大学人口健康科学研究所）
Phase I dose-finding trials in oncology seek to find the maximum tolerated dose (MTD) of a drug under a specific schedule. Evaluating drug-schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose-regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) information to estimate the maximum tolerated dose-regimen (MTD-regimen), at the end of the dose-escalati…
Basket trials have emerged as a new class of efficient approaches in oncology to evaluate a new treatment in several patient subgroups simultaneously. In this paper, we extend the key ideas to disease areas outside of oncology, developing a robust Bayesian methodology for randomised, placebo-controlled basket trials with a continuous endpoint to enable borrowing of information across subtrials with similar treatment effects. After adjusting for covariates, information from a complementary subtrial can be represented into a commensurate prior for the parameter that underpins the subtrial under …
This manuscript introduces an \proglang{R} package called \pkg{trialr} that implements a collection of clinical trial methods in \proglang{Stan} and \proglang{R}. In this article, we explore three methods in detail. The first is the continual reassessment method for conducting phase I dose-finding trials that seek a maximum tolerable dose. The second is EffTox, a dose-finding design that scrutinises doses by joint efficacy and toxicity outcomes. The third is the augmented binary method for modelling the probability of treatment success in phase II oncology trials with reference to repeated mea…
Basket designs are prospective clinical trials that are devised with the hypothesis that the presence of selected molecular features determine a patient's subsequent response to a particular "targeted" treatment strategy. Basket trials are designed to enroll multiple clinical subpopulations to which it is assumed that the therapy in question offers beneficial efficacy in the presence of the targeted molecular profile. The treatment, however, may not offer acceptable efficacy to all subpopulations enrolled. Moreover, for rare disease settings, such as oncology wherein these trials have become p…
Determining the extent to which a patient is benefiting from cancer therapy is challenging. Criteria for quantifying the extent of "tumor response" observed within a few cycles of treatment have been established for various types of solid as well as hematologic malignancies. These measures comprise the primary endpoints of phase II trials. Regulatory approvals of new cancer therapies, however, are usually contingent upon the demonstration of superior overall survival with randomized evidence acquired with a phase III trial comparing the novel therapy to an appropriate standard of care treatmen…
In many phase II trials in solid tumours, patients are assessed using endpoints based on the Response Evaluation Criteria in Solid Tumours (RECIST) scale. Often, analyses are based on the response rate. This is the proportion of patients who have an observed tumour shrinkage above a pre-defined level and no new tumour lesions. The augmented binary method has been proposed to improve the precision of the estimator of the response rate. The method involves modelling the tumour shrinkage to avoid dichotomising it. However, in many trials the best observed response is used as the primary outcome. …
Hybrid Non-informative and Informative Prior Model-assisted Designs for Mid-trial Dose Insertion
Institute of Science Tokyo（东京科学大学）；The University of Osaka（大阪大学）；Astellas Pharma Global Development Inc.（安斯泰来制药全球开发株式会社）；Wakayama Medical University（和歌山医科大学）；Chuo University（中央大学）；The Institute of Statistical Mathematics（统计数学研究所）
Quantifying Uncertainty in Future Event Counts at Interim Analyses of Time-to-Event Trials, with an Application to Pediatric Oncology
Bicocca Bioinformatics, Biostatistics and Bioimaging Centre B4School of Medicine and Surgery, University of Milano–Bicocca（米兰-比可卡大学医学与外科学院）；University of Warwick（华威大学）；Fondazione IRCCS San Gerardo dei Tintori（圣杰拉尔多蒂托里IRCCS基金会）
Clinical trials usually involve sequential patient entry. When designing a clinical trial, it is often desirable to include a provision for interim analyses of accumulating data with the potential for stopping the trial early. We review Bayesian sequential clinical trial designs based on posterior probabilities, posterior predictive probabilities, and decision-theoretic frameworks. A pertinent question is whether Bayesian sequential designs need to be adjusted for the planning of interim analyses. We answer this question from three perspectives: a frequentist-oriented perspective, a calibrated…
Efforts to develop biomarker-targeted anti-cancer therapies have progressed rapidly in recent years. Six antibodies acting on programmed death ligand 1 or programmed death 1 pathways were approved in 75 cancer indications between 2015 and 2021. With efforts to expedite regulatory reviews of promising therapies, several targeted cancer therapies have been granted accelerated approval on the basis of single-arm phase II trials. And yet, in the absence of randomization, patient outcomes may not have been studied under standard of care chemotherapies for emerging biomarker subpopulations prior to …
One-sample survival tests in the presence of non-proportional hazards in oncology clinical trials
Oncostat, CESP, INSERM U1018（INSERM U1018）；University Paris-Saclay（巴黎萨克雷大学）；Department of Statistics and Acturial Science（统计与精算学系）；University of Hong Kong（香港大学）
Centrality in Collaboration: A Novel Algorithm for Social Partitioning Gradients in Community Detection for Multiple Oncology Clinical Trial Enrollments
University of Toronto（多伦多大学）；University Health Network（大学健康网络）
Dose-finding clinical trials in oncology aim to determine the maximum tolerated dose (MTD) of a new drug, generally defined by the proportion of patients with short-term dose-limiting toxicities (DLTs). Model-based approaches for such phase I oncology trials have been widely designed and are mostly restricted to the DLTs occurring during the first cycle of treatment, although patients continue to receive treatment for multiple cycles. We aim to estimate the probability of DLTs over sequences of treatment cycles via a Bayesian cumulative modeling approach, where the probability of DLT is modele…
Recently, the strategy for dose optimization in oncology has shifted to conduct Phase 2 randomized controlled trials with multiple doses. Optimal biologic dose selection from Phase 1 trial data to determine candidate doses for Phase 2 trials has been gaining attention. This study proposes a novel adaptive cohort size determination method for optimal biologic dose-finding to accelerate trials. The cohort size expansion is determined adaptively depending on the toxicity and efficacy data of the ongoing trial. In a simulation, the proposed method shortened the trial duration and maintained accura…
