TITE‐gBOIN : Time‐to‐event Bayesian optimal interval design to accelerate dose‐finding accounting for toxicity grades

2021 ◽  
Author(s):  
Kentaro Takeda ◽  
Qing Xia ◽  
Shufang Liu ◽  
Alan Rong
Keyword(s):  
Dose Finding ◽  
Time To Event ◽  
Optimal Interval

scholarly journals Adaptive Design For Identifying Maximum Tolerated Dose Early To Accelerate Dose-Finding Trial

2021 ◽  
Author(s):  
Masahiro Kojima
Keyword(s):  
Simulation Study ◽  
Early Identification ◽  
Adaptive Design ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Time To Event ◽  
Optimal Interval ◽  
Study Duration ◽  
Event Model ◽  
Expansion Cohort

Abstract Purpose: The early identification of maximum tolerated dose (MTD) in phase I trial leads to faster progression to a phase II trial or an expansion cohort to confirm efficacy.Methods: We propose a novel adaptive design for identifying MTD early to accelerate dose-finding trials. The early identification of MTD is determined adaptively by dose-retainment probability using a trial data via Bayesian analysis. We applied the early identification design to an actual trial. A simulation study evaluates the performance of the early identification design.Results: In the actual study, we confirmed the MTD could be early identified and the study period was shortened. In the simulation study, the percentage of the correct MTD selection in the early identification Keyboard and early identification Bayesian optimal interval (BOIN) designs was almost same from the non-early identification version. The early identification Keyboard and BOIN designs reduced the study duration by about 50% from the model-assisted designs. In addition, the early identification Keyboard and BOIN designs reduced the study duration by about 20% from time-to-event model-assisted designs.Conclusion: We proposed the early identification of MTD maintaining the accuracy to be able to short the study period.

scholarly journals Would the Recommended Dose Have Been Different Using Novel Dose-Finding Designs? Comparing Dose-Finding Designs in Published Trials

2021 ◽  
pp. 1024-1034
Author(s):  
Rebecca B. Silva ◽  
Christina Yap ◽  
Richard Carvajal ◽  
Shing M. Lee
Keyword(s):  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Simulation Studies ◽  
Toxicity Data ◽  
Continual Reassessment Method ◽  
Optimal Interval ◽  
Patient Allocation ◽  
Continual Reassessment ◽  
Continuous Use ◽  
Dose Levels

PURPOSE Simulation studies have shown that novel designs such as the continual reassessment method and the Bayesian optimal interval (BOIN) design outperform the 3 + 3 design by recommending the maximum tolerated dose (MTD) more often, using less patients, and allotting more patients to the MTD. However, it is not clear whether these novel designs would have yielded different results in the context of real-world dose-finding trials. This is a commonly mentioned reason for the continuous use of 3 + 3 designs for oncology trials, with investigators considering simulation studies not sufficiently convincing to warrant the additional design complexity of novel designs. METHODS We randomly sampled 60 published dose-finding trials to obtain 22 that used the 3 + 3 design, identified an MTD, published toxicity data, and had more than two dose levels. We compared the published MTD with the estimated MTD using the continual reassessment method and BOIN using target toxicity rates of 25% and 30% and toxicity data from the trial. Moreover, we compared patient allocation and sample size assuming that these novel designs had been implemented. RESULTS Model-based designs chose dose levels higher than the published MTD in about 40% of the trials, with estimated and observed toxicity rates closer to the target toxicity rates of 25% and 30%. They also assigned less patients to suboptimal doses and permitted faster dose escalation. CONCLUSION This study using published dose-finding trials shows that novel designs would recommend different MTDs and confirms the advantages of these designs compared with the 3 + 3 design, which were demonstrated by simulation studies.

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 Time-to-event model-assisted designs for dose-finding trials with delayed toxicity

Biostatistics ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 807-824 ◽  
Author(s):  
Ruitao Lin ◽  
Ying Yuan
Keyword(s):  
Late Onset ◽  
Dose Finding ◽  
Time To Event ◽  
Trial Conduct ◽  
Accrual Rate ◽  
Complicated Model ◽  
Speed Up ◽  
Event Model ◽  
User Friendly ◽  
Patient Accrual

Summary Two useful strategies to speed up drug development are to increase the patient accrual rate and use novel adaptive designs. Unfortunately, these two strategies often conflict when the evaluation of the outcome cannot keep pace with the patient accrual rate and thus the interim data cannot be observed in time to make adaptive decisions. A similar logistic difficulty arises when the outcome is late-onset. Based on a novel formulation and approximation of the likelihood of the observed data, we propose a general methodology for model-assisted designs to handle toxicity data that are pending due to fast accrual or late-onset toxicity and facilitate seamless decision making in phase I dose-finding trials. The proposed time-to-event model-assisted designs consider each dose separately and the dose-escalation/de-escalation rules can be tabulated before the trial begins, which greatly simplifies trial conduct in practice compared to that under existing methods. We show that the proposed designs have desirable finite and large-sample properties and yield performance that is comparable to that of more complicated model-based designs. We provide user-friendly software for implementing the designs.

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

scholarly journals Consequences of Performing Parallel Dose Finding Trials in Heterogeneous Groups of Patients

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Bethany Jablonski Horton ◽  
John O'Quigley ◽  
Mark R Conaway
Keyword(s):  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Operating Characteristics ◽  
Dose Selection ◽  
Partially Ordered ◽  
Optimal Interval ◽  
Multiple Group ◽  
Ordered Groups ◽  
Toxicity Risk ◽  
Dose Recommendations

Abstract Patient heterogeneity, in which patients can be grouped by risk of toxicity, is a design challenge in early phase dose finding trials. Carrying out independent trials for each group is a readily available approach for dose finding. However, this often leads to dose recommendations that violate the known order of toxicity risk by group, or reversals in dose recommendation. In this manuscript, trials for partially ordered groups are simulated using four approaches: independent parallel trials using the continual reassessment method (CRM), Bayesian optimal interval design, and 3 + 3 methods, as well as CRM for partially ordered groups. Multiple group order structures are considered, allowing for varying amounts of group frailty order information. These simulations find that parallel trials in the presence of partially ordered groups display a high frequency of trials resulting in reversals. Reversals occur when dose recommendations do not follow known order of toxicity risk by group, such as recommending a higher dose level in a group of patients known to have a higher risk of toxicity. CRM for partially ordered groups eliminates the issue of reversals, and simulation results indicate improved frequency of maximum tolerated dose selection as well as treating a greater proportion of trial patients at this dose compared with parallel trials. When information is available on differences in toxicity risk by patient subgroup, methods designed to account for known group ordering should be considered to avoid reversals in dose recommendations and improve operating characteristics.

scholarly journals A Dose-Finding Trial for Hyperthermic Intraperitoneal Cisplatin in Gynecological Cancer Patients Receiving Hyperthermic Intraperitoneal Chemotherapy

2021 ◽  
Vol 11 ◽  
Author(s):  
Chui-ying Chan ◽  
Hui Li ◽  
Miao-fang Wu ◽  
Chang-hao Liu ◽  
Huai-wu Lu ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Dose Level ◽  
Gynecological Cancer ◽  
Gynecologic Cancer ◽  
Kidney Injury ◽  
Maximum Tolerated Dose ◽  
Subsequent Treatment ◽  
Dose Finding ◽  
Optimal Interval ◽  
Chronic Kidney Injury

Background: To identify the maximum tolerated dose (MTD) of hyperthermic intraperitoneal cisplatin at 43°C among gynecological cancer patients.Methods: In this Phase I dose-finding trial, Bayesian optimal interval (BOIN) design was used. We sought to explore the MTD with a target dose-limiting toxicity (DLT) rate of 20%, 4 prespecified doses (70 mg/m2, 75 mg/m2, 80 mg/m2 and 85 mg/m2), and 30 patients.Results: Between 2019 and 2020, 30 gynecologic cancer patients were enrolled. No patients received bevacizumab in subsequent treatment. The most common adverse events related to cisplatin were nausea and vomiting (100%), followed by tinnitus (26.7%) and kidney injury (23.3%). Of the seven patients with kidney injury, four had persistent renal impairment, and finally progressed into chronic kidney injury. DLTs were noted only in the dose level 4 group (85 mg/m2) and included acute kidney injury, pulmonary embolism, anemia, and neutropenia. When cisplatin was given at dose level four (85 mg/m2), the isotonic estimate of the DLT rate (22%) was closest to the target DLT rate of 20%. Therefore, 85 mg/m2 was selected as the MTD, with a 51% probability that the toxicity probability was greater than the target DLT rate.Conclusions: For gynecological cancer patients who received HIPEC for peritoneal metastases, the MTD of cisplatin in HIPEC at 43°C was 85 mg/m2. Our findings apply to patients who do not receive bevacizumab (ChiCTR1900021555).

Sign in / Sign up

Export Citation Format

Share Document