Probability-of-decision interval 3+3 (POD-i3+3) design for phase I dose finding trials with late-onset toxicity

2021 ◽  
pp. 096228022110527
Author(s):  
Zichun Xu ◽  
Xiaolei Lin
Keyword(s):  
Phase I ◽  
Late Onset ◽  
Dose Finding ◽  
Operating Characteristics ◽  
Trial Duration ◽  
Probability Interval ◽  
Optimal Interval ◽  
Patient Resources ◽  
Safety And Reliability

Late-onset toxicities often occur in phase I trials investigating novel immunotherapy and molecular targeted therapies. For trials with cohort based designs (such as modified toxicity probability interval, Bayesian optimal interval, and i3+3), patients are often turned away since the current cohort are still being followed without definite dose-limiting toxicities, which results in prolonged trial duration and waste of patient resources. In this paper, we incorporate a probability-of-decision framework into the i3+3 design and allow real-time dosing inference when the next patient becomes available. Both follow-up time for the pending patients and time to dose-limiting toxicities for the observed patients are used in calculating the posterior probability of each possible dosing decision. An intensive simulation study is conducted to evaluate the operating characteristics of the newly proposed probability-of-decision-i3+3 design under various dosing scenarios and patient accrual settings. Results show that the probability-of-decision-i3+3 design achieves comparable safety and reliability performances but much shorter trial duration compared to the complete designs.

scholarly journals Adaptive Dose-Finding Studies: A Review of Model-Guided Phase I Clinical Trials

2014 ◽  
Vol 32 (23) ◽  
pp. 2505-2511 ◽  
Author(s):  
Alexia Iasonos ◽  
John O'Quigley
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Escalation ◽  
Late Onset ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Design Parameters ◽  
Phase I Trials ◽  
Phase I Clinical Trials ◽  
Trial Duration

Purpose We provide a comprehensive review of adaptive phase I clinical trials in oncology that used a statistical model to guide dose escalation to identify the maximum-tolerated dose (MTD). We describe the clinical setting, practical implications, and safety of such applications, with the aim of understanding how these designs work in practice. Methods We identified 53 phase I trials published between January 2003 and September 2013 that used the continual reassessment method (CRM), CRM using escalation with overdose control, or time-to-event CRM for late-onset toxicities. Study characteristics, design parameters, dose-limiting toxicity (DLT) definition, DLT rate, patient-dose allocation, overdose, underdose, sample size, and trial duration were abstracted from each study. In addition, we examined all studies in terms of safety, and we outlined the reasons why escalations occur and under what circumstances. Results On average, trials accrued 25 to 35 patients over a 2-year period and tested five dose levels. The average DLT rate was 18%, which is lower than in previous reports, whereas all levels above the MTD had an average DLT rate of 36%. On average, 39% of patients were treated at the MTD, and 74% were treated at either the MTD or an adjacent level (one level above or below). Conclusion This review of completed phase I studies confirms the safety and generalizability of model-guided, adaptive dose-escalation designs, and it provides an approach for using, interpreting, and understanding such designs to guide dose escalation in phase I trials.

CWL: A conditional weighted likelihood method to account for the delayed joint toxicity–efficacy outcomes for phase I/II clinical trials

2020 ◽  
pp. 096228022097932
Author(s):  
Yifei Zhang ◽  
Yong Zang
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Joint Probability ◽  
Dose Finding ◽  
Likelihood Method ◽  
Joint Toxicity ◽  
Weighted Likelihood ◽  
Operating Characteristics ◽  
Model Assumption

The delayed outcome issue is common in early phase dose-finding clinical trials. This problem becomes more intractable in phase I/II clinical trials because both toxicity and efficacy responses are subject to the delayed outcome issue. The existing methods applying for the phase I trials cannot be used directly for the phase I/II trial due to a lack of capability to model the joint toxicity–efficacy distribution. In this paper, we propose a conditional weighted likelihood (CWL) method to circumvent this issue. The key idea of the CWL method is to decompose the joint probability into the product of marginal and conditional probabilities and then weight each probability based on each patient’s actual follow-up time. The CWL method makes no parametric model assumption on either the dose–response curve or the toxicity–efficacy correlation and therefore can be applied to any existing phase I/II trial design. Numerical trial applications show that the proposed CWL method yields desirable operating characteristics.

scholarly journals BOIN12: Bayesian Optimal Interval Phase I/II Trial Design for Utility-Based Dose Finding in Immunotherapy and Targeted Therapies

2020 ◽  
pp. 1393-1402
Author(s):  
Ruitao Lin ◽  
Yanhong Zhou ◽  
Fangrong Yan ◽  
Daniel Li ◽  
Ying Yuan
Keyword(s):  
Phase I ◽  
Trial Design ◽  
Checkpoint Inhibitors ◽  
Optimal Dose ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Operating Characteristics ◽  
T Cell Therapy ◽  
Trade Off ◽  
Optimal Interval

PURPOSE For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients’ risk-benefit trade-off. METHODS We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design. RESULTS Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation. CONCLUSION The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.

scholarly journals Phase I/II dose-finding design for molecularly targeted agent: Plateau determination using adaptive randomization

2016 ◽  
Vol 27 (2) ◽  
pp. 466-479 ◽  
Author(s):  
Marie-Karelle Riviere ◽  
Ying Yuan ◽  
Jacques-Henri Jourdan ◽  
Frédéric Dubois ◽  
Sarah Zohar
Keyword(s):  
Phase I ◽  
Late Onset ◽  
Optimal Dose ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Targeted Agents ◽  
Weighted Likelihood ◽  
Operating Characteristics ◽  
Phase I Clinical Trials ◽  
Molecularly Targeted Agents

Conventionally, phase I dose-finding trials aim to determine the maximum tolerated dose of a new drug under the assumption that both toxicity and efficacy monotonically increase with the dose. This paradigm, however, is not suitable for some molecularly targeted agents, such as monoclonal antibodies, for which efficacy often increases initially with the dose and then plateaus. For molecularly targeted agents, the goal is to find the optimal dose, defined as the lowest safe dose that achieves the highest efficacy. We develop a Bayesian phase I/II dose-finding design to find the optimal dose. We employ a logistic model with a plateau parameter to capture the increasing-then-plateau feature of the dose–efficacy relationship. We take the weighted likelihood approach to accommodate for the case where efficacy is possibly late-onset. Based on observed data, we continuously update the posterior estimates of toxicity and efficacy probabilities and adaptively assign patients to the optimal dose. The simulation studies show that the proposed design has good operating characteristics. This method is going to be applied in more than two phase I clinical trials as no other method is available for this specific setting. We also provide an R package dfmta that can be downloaded from CRAN website.

BOIN: A novel Bayesian design platform to accelerate early phase brain tumor clinical trials

2021 ◽  
Author(s):  
Ying Yuan ◽  
Jing Wu ◽  
Mark R Gilbert
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Early Phase ◽  
Late Onset ◽  
Single Agent ◽  
Phase Iii ◽  
Combination Regimen ◽  
Operating Characteristics ◽  
Optimal Interval ◽  
Phase Iii Trials

Abstract Despite decades of extensive research, the progress in developing effective treatments for primary brain tumors lags behind that of other cancers, largely due to the unique challenges of brain tumors (e.g., the blood-brain barrier and high heterogeneity) that limit the delivery and efficacy of many therapeutic agents. One way to address this issue is to employ novel trial designs to better optimize the treatment regimen (e.g., dose and schedule) in early phase trials to improve the success rate of subsequent phase III trials. The objective of this article is to introduce Bayesian optimal interval (BOIN) designs as a novel platform to design various types of early phase brain tumor trials, including single-agent and combination regimen trials, trials with late-onset toxicities, and trials aiming to find the optimal biological dose (OBD) based on both toxicity and efficacy. Unlike many novel Bayesian adaptive designs, which are difficult to understand and complicated to implement by clinical investigators, the BOIN designs are self-explanatory and user friendly, yet yield more robust and powerful operating characteristics than conventional designs. We illustrate the BOIN designs using a phase I clinical trial of brain tumor, and provide software (freely available at www.trialdesign.org) to facilitate the application of the BOIN design.

scholarly journals Effective sample size for computing prior hyperparameters in Bayesian phase I–II dose-finding

2014 ◽  
Vol 11 (6) ◽  
pp. 657-666 ◽  
Author(s):  
Peter F Thall ◽  
Richard C Herrick ◽  
Hoang Q Nguyen ◽  
John J Venier ◽  
J Clift Norris
Keyword(s):  
Phase I ◽  
Sample Size ◽  
Dose Finding ◽  
Design Parameters ◽  
Effective Sample Size ◽  
Operating Characteristics ◽  
Trade Off ◽  
Design Elements ◽  
Practical Usefulness ◽  
Practical Guidelines

Background: The efficacy–toxicity trade-off based design is a practical Bayesian phase I–II dose-finding methodology. Because the design’s performance is very sensitive to prior hyperparameters and the shape of the target trade-off contour, specifying these two design elements properly is essential. Purpose: The goals are to provide a method that uses elicited mean outcome probabilities to derive a prior that is neither overly informative nor overly disperse, and practical guidelines for specifying the target trade-off contour. Methods: A general algorithm is presented that determines prior hyperparameters using least squares penalized by effective sample size. Guidelines for specifying the trade-off contour are provided. These methods are illustrated by a clinical trial in advanced prostate cancer. A new version of the efficacy–toxicity program is provided for implementation. Results: Together, the algorithm and guidelines provide substantive improvements in the design’s operating characteristics. Limitations: The method requires a substantial number of elicited values and design parameters, and computer simulations are required to obtain an acceptable design. Conclusion: The two key improvements greatly enhance the efficacy–toxicity design’s practical usefulness and are straightforward to implement using the updated computer program. The algorithm for determining prior hyperparameters to ensure a specified level of informativeness is general, and may be applied to models other than that underlying the efficacy–toxicity method.

scholarly journals A90 A UTILITY-BASED BAYESIAN OPTIMAL INTERVAL (U-BOIN) PHASE I/II DESIGN TO IDENTIFY THE OPTIMAL BIOLOGICAL DOSE FOR TARGETED AND IMMUNE THERAPIES

2020 ◽  
Vol 3 (Supplement_1) ◽  
pp. 104-105
Author(s):  
Y Zhou ◽  
J Lee ◽  
Y Yuan
Keyword(s):  
Phase I ◽  
Model Fitting ◽  
Maximum Tolerated Dose ◽  
Complex Model ◽  
Dose Finding ◽  
Optimal Interval ◽  
Funding Agencies ◽  
Immune Therapies ◽  
Biological Dose ◽  
Decision Tables

Abstract Background In the era of targeted therapy and immunotherapy, the objective of dose finding is often to identify the optimal biological dose (OBD), rather than the maximum tolerated dose (MTD). Aims To develop a utility-based Bayesian optimal interval (U-BOIN) phase I/II design to find the OBD. Methods We jointly model toxicity and efficacy using a multinomial-Dirichlet model, and employ a utility function to measure dose risk-benefit trade-off. The U-BOIN design consists of two seamlessly connected stages. In stage I, the Bayesian optimal interval (BOIN) design is used to quickly explore the dose space and collect preliminary toxicity and efficacy data. In stage II, in light of accumulating efficacy and toxicity from both stages I and II, we continuously update the posterior estimate of the utility for each dose after each cohort, and use this information to direct the dose assignment and selection. Compared to existing phase I/II designs, one prominent advantage of the U-BOIN design is its simplicity for implementation. Once the trial is designed, it can be easily applied using predetermined decision tables, without complex model fitting and estimation. Results Our simulation study shows that, despite its simplicity, the U-BOIN design is robust and has high accuracy to identify the OBD. Conclusions The U-BOIN design provide a practical, easy-to-implement method to identify the OBD for phase I-II clinical trials. It has great potential to accelate the drug development for GI diseases. Funding Agencies NIH

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