Heng Zhou

Merck & Co., Inc., Rahway, NJ, USA (United States)

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Ying Yuan

The University of Texas MD Anderson Cancer Center

Cong Chen

Merck & Co., Inc., Rahway, NJ, USA (United States)

Elisha J. Dettman

Merck & Co., Inc., Rahway, NJ, USA (United States)

Cai Wu

Second Affiliated Hospital of Soochow University

Alexander Gozman

Merck & Co., Inc., Rahway, NJ, USA (United States)

Linda Sun

Decision Sciences (United States)

Rǎzvan Cristescu

Merck & Co., Inc., Rahway, NJ, USA (United States)

Changxia Shao

Merck & Co., Inc., Rahway, NJ, USA (United States)

Liam C. Lee

University of Cambridge

Lei Nie

United States Food and Drug Administration

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Merck & Co., Inc., Rahway, NJ, USA (United States)

The University of Texas MD Anderson Cancer Center

Decision Sciences (United States)

Memorial Sloan Kettering Cancer Center

Inserm

Sichuan University

Shanghai Jiao Tong University

Cornell University

Dana-Farber Cancer Institute

Johns Hopkins University

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Bayesian optimal phase II clinical trial design with time‐to‐event endpoint

Pharmaceutical Statistics (2020)

Heng Zhou Cong Chen Linda Sun Ying Yuan

SUMMARY We propose a Bayesian optimal phase II (BOP2) design for clinical trials with a time‐to‐event endpoint (eg, progression‐free survival [PFS]) or co‐primary endpoints consisted of a time‐to‐event endpoint and a categorical endpoint (eg, PFS and toxicity). We use an exponential‐inverse gamma model to model the time to event. At each interim, the go/no‐go decision is made by comparing the posterior probabilities of the event of interest with an adaptive probability cutoff. The BOP2 design is flexible in the number of interim looks and applicable to both single‐arm and two‐arm trials. The design maximizes the power for detecting effective treatments, with a well‐controlled type I error, thereby bridging the gap between Bayesian designs and frequentist designs. The BOP2 design is easy to implement. Its stopping boundary can be enumerated and included in study protocol before the onset of the trial for single‐arm studies. Simulation studies show that the BOP2 design has favorable operating characteristics, with higher power and lower risk of incorrectly terminating the trial than some Bayesian phase II designs. The software to implement the BOP2 design will be freely available at www.trialdesign.org .

Categorical variable

Early stopping

Interim analysis

Interim

Clinical endpoint

Clinical study design

10.1002/pst.2030

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Citations (17)

BOP2-TE: Bayesian Optimal Phase 2 Design for Jointly Monitoring Efficacy and Toxicity with Application to Dose Optimization

arXiv (Cornell University) (2024)

Kai Chen Heng Zhou J. Jack Lee Ying Yuan

We propose a Bayesian optimal phase 2 design for jointly monitoring efficacy and toxicity, referred to as BOP2-TE, to improve the operating characteristics of the BOP2 design proposed by Zhou et al. (2017). BOP2-TE utilizes a Dirichlet-multinomial model to jointly model the distribution of toxicity and efficacy endpoints, making go/no-go decisions based on the posterior probability of toxicity and futility. In comparison to the original BOP2 and other existing designs, BOP2-TE offers the advantage of providing rigorous type I error control in cases where the treatment is toxic and futile, effective but toxic, or safe but futile, while optimizing power when the treatment is effective and safe. As a result, BOP2-TE enhances trial safety and efficacy. We also explore the incorporation of BOP2-TE into multiple-dose randomized trials for dose optimization, and consider a seamless design that integrates phase I dose finding with phase II randomized dose optimization. BOP2-TE is user-friendly, as its decision boundary can be determined prior to the trial's onset. Simulations demonstrate that BOP2-TE possesses desirable operating characteristics. We have developed a user-friendly web application as part of the BOP2 app, which is freely available at www.trialdesign.org.

Bayesian Optimization

Maximum tolerated dose

10.48550/arxiv.2408.05816

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Citations (0)

Comparative review of novel model‐assisted designs for phase I clinical trials

Statistics in Medicine (2018)

Heng Zhou Thomas A. Murray Haitao Pan Ying Yuan

A number of novel phase I trial designs have been proposed that aim to combine the simplicity of algorithm‐based designs with the superior performance of model‐based designs, including the modified toxicity probability interval, Bayesian optimal interval, and Keyboard designs. In this article, we review these “model‐assisted” designs, contrast their statistical foundations and pros and cons, and compare their operating characteristics with the continual reassessment method. To provide unbiased and reliable results, our comparison is based on 10 000 dose‐toxicity scenarios randomly generated using the pseudo‐uniform algorithm recently proposed in the literature. The results showed that the continual reassessment method, Bayesian optimal interval, and Keyboard designs provide comparable, superior operating characteristics, and each outperforms the modified toxicity probability interval design. These designs are more likely to correctly select the maximum tolerated dose and less likely to overdose patients.

Maximum tolerated dose

Optimal design

Clinical study design

10.1002/sim.7674

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Citations (68)

Association Between Homologous Recombination Repair Biomarkers and Survival in Patients With Solid Tumors

JCO Precision Oncology (2023)

Changxia Shao Yixin Ren Heng Zhou Cai Chen Elisha J. Dettman

Mutations in

10.1200/po.23.00195

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Citations (1)

Dose Selection with 2-in-1 Design

ICSA book series in statistics (2024)

Eric Zhang Hui Tang Zhiyuan Liu Miao Yang Qiqi Deng

10.1007/978-3-031-67110-4_7

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Citations (0)

Bayesian Designs for Early Phase Clinical Trials with Novel Target Agents

Heng Zhou

Source

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A Generalized Framework of Optimal Two-Stage Designs for Exploratory Basket Trials

Statistics in Biopharmaceutical Research (2021)

Xiaoqiang Wu Cai Wu Fang Liu Heng Zhou Cong Chen

The primary goal of an exploratory oncology clinical trial is to identify an effective drug for further development. To expedite the drug development process and increase the chance of finding active tumor indications where the treatment works, multiple tumor cohorts are often investigated simultaneously in a basket trial. In this article, we propose a generalized framework of an optimal basket trial design in the exploratory setting where tumor indications can be homogeneous (objective response rates of all indications are the same under both the null and alternative hypotheses) or heterogeneous (objective response rates are not the same under at least one of the null or alternative hypotheses). The proposed design prunes the inactive tumor indication in Stage 1 and pools the remaining tumor indications at the end of Stage 2 to evaluate the overall effectiveness of whether the treatment works in at least one tumor indication. The design parameters are optimized to minimize the expected sample size while explicitly controlling the global Type I and Type II error rates. In addition, we consider reallocating the planned Stage 2 sample size of pruned indications to achieve a higher power when the total planned sample size is fixed. Simulation studies are conducted to show the favorable operating characteristics of the proposed design under certain scenarios.

Exploratory research

Clinical study design

Statistical power

10.1080/19466315.2021.1906741

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Citations (8)

Statistical and practical considerations in planning and conduct of dose-optimization trials

Clinical Trials (2024)

Ying Yuan Heng Zhou Suyu Liu

The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.

Maximum tolerated dose

10.1177/17407745231207085

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Citations (3)

Statistical Considerations of Designs for Single-Arm Proof-of-Concept Oncology Trial with Time-to-Event Endpoint

Heng Zhou Linda Sun Meihua Wang

Single-arm proof-of-concept (PoC) clinical studies are widely used to accelerate the signal-finding process in oncology drug development before or in lieu of randomized PoC studies. Traditionally the primary endpoint for single-arm PoC studies is objective response rate (ORR). However, in cases that ORR is not applicable or not clinically relevant, time-to-event (TTE) endpoint is used instead. One conventional approach is to dichotomize the TTE endpoint as a binary endpoint to assess the survival rate, which may compromise the testing efficiency due to the requirement of minimum follow-up without censoring. Alternatively, we can use the non-parametric one-sample log-rank test (OSLRT) to evaluate the survival curve difference compared with historical control. This approach can incorporate censoring and all time-point information on the survival curve, but the test statistic may be difficult to interpret and quantify the magnitude of treatment effect. Given that clinicians are more interested in the survival rate at a clinically relevant landmark timepoint, we can also use a landmark Kaplan-Meier method (LMKM) to estimate the survival rate at a landmark timepoint for design and analysis of single-arm proof-of-concept oncology trials with TTE endpoint. This non-parametric method is straightforward to clinicians and shows favorable operating characteristics from simulation studies.Â We also developed an R package for the implementation of these mainstream designs, which fills the gap of available software for design and analysis of single-arm studies with TTE endpoint.

Proof of concept

Clinical endpoint

10.2139/ssrn.4329432

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How Many Cohorts Should Be Considered in an Exploratory Master Protocol?

Statistics in Biopharmaceutical Research (2020)

Cong Chen Heng Zhou Wen Li Robert A. Beckman

We consider a simplified exploratory master protocol in oncology that intends to investigate multiple test drugs in the same tumor indication (i.e., an umbrella trial) or investigate the same test drug in multiple tumor indications (i.e., a basket trial). The primary objective of an exploratory umbrella trial is to test whether any of the drugs is effective, whereas individual drug cohorts are analyzed independently. In contrast, the primary objective of an exploratory basket trial is to test whether the test drug is effective in any of the tumor indications, whereas data are subject to pooling. The distinction is less explored in literature and is often overlooked in practice. In this research note, we illustrate it with the pruning-and-pooling method that was first proposed for designing confirmatory basket trials. The method is extended to address a high-level strategic question: how many cohorts (number of drugs in an umbrella trial or number of tumor indications in a basket trial) should be considered in each master protocol under a resource constraint?

10.1080/19466315.2020.1841022

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Citations (2)