Bayesian Adaptive Design for Finding the Maximum Tolerated Sequence of Doses in Multicycle Dose-Finding Clinical Trials

Purpose Statistical designs for traditional phase I dose-finding trials consider dose-limiting toxicity in the first cycle of treatment. In reality, patients often go through multiple cycles of treatment and may experience toxicity events in more than one cycle. Therefore, it is desirable to identify the maximum tolerated sequence of three doses across three cycles of treatment. Methods Motivated by a three-cycle dose-finding clinical trial for a rare cancer with a JAK inhibitor, we proposed and implemented a simple Bayesian adaptive dose-cycle finding (BaSyc) design that allows intercycle and intrapatient dose modification. Because of the patient-specific dosing strategy over cycles, the BaSyc design is suited as a method in precision oncology. Results BaSyc is simple and transparent because its algorithm can be summarized as two tabulated decision rules before the trial starts, allowing physicians to visually examine these rules. In addition, BaSyc employs a time-saving enrollment scheme that speeds up the trial. Extensive simulation studies show that BaSyc has desirable operating characteristics in identifying the maximum tolerated sequence. Conclusion The BaSyc design provides a first-of-kind multicycle approach for dose finding and will likely lead to better and safer patient care and drug development.

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A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate

Journal of Probability and Statistics ◽

10.1155/2018/8654173 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Márcio Augusto Diniz ◽

Sungjin Kim ◽

Mourad Tighiouart

Keyword(s):

Phase I ◽

Adaptive Design ◽

Cytotoxic Agents ◽

Dose Finding ◽

Model Parameters ◽

Baseline Characteristic ◽

Phase I Clinical Trials ◽

Bayesian Adaptive Design ◽

Dose Combination ◽

Toxicity Relationship

A Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility is described. The estimation of the maximum tolerated dose (MTD) curve is a function of a baseline covariate using two cytotoxic agents. A logistic model is used to describe the relationship between the doses, baseline covariate, and the probability of dose limiting toxicity (DLT). Trial design proceeds by treating cohorts of two patients simultaneously using escalation with overdose control (EWOC), where at each stage of the trial, the next dose combination corresponds to the α quantile of the current posterior distribution of the MTD of one of two agents at the current dose of the other agent and the next patient’s baseline covariate value. The MTD curves are estimated as function of Bayes estimates of the model parameters at the end of trial. Average DLT, pointwise average bias, and percent of dose recommendation at dose combination neighborhoods around the true MTD are compared between the design that uses the covariate and the one that ignores the baseline characteristic. We also examine the performance of the approach under model misspecifications for the true dose-toxicity relationship. The methodology is further illustrated in the case of a prespecified discrete set of dose combinations.

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A combined proof of concept and dose finding study with multiple endpoints: A Bayesian adaptive design in chronic prostatitis/chronic pelvic pain syndrome

Biometrical Journal ◽

10.1002/bimj.201700210 ◽

2018 ◽

Vol 61 (3) ◽

pp. 476-487 ◽

Cited By ~ 1

Author(s):

Reynaldo Martina ◽

Jos Houbiers ◽

Joost Melis ◽

Olivier Till

Keyword(s):

Adaptive Design ◽

Chronic Pelvic Pain Syndrome ◽

Pain Syndrome ◽

Dose Finding ◽

Pelvic Pain Syndrome ◽

Proof Of Concept ◽

Multiple Endpoints ◽

Bayesian Adaptive Design ◽

Finding Study ◽

Dose Finding Study

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Early-Phase Platform Trials: A New Paradigm for Dose Finding and Treatment Screening in the Era of Precision Oncology

JCO Precision Oncology ◽

10.1200/po.19.00057 ◽

2019 ◽

pp. 1-8

Author(s):

Mei-Yin C. Polley ◽

Ying Kuen Cheung

Keyword(s):

Early Phase ◽

Cytotoxic Agents ◽

Dose Finding ◽

Stopping Rules ◽

Precision Oncology ◽

Cancer Therapies ◽

New Paradigm ◽

Cancer Trial ◽

Targeted Cancer Therapies ◽

Statistical Designs

Applications in early-phase cancer trials have motivated the development of many statistical designs since the late 1980s, including dose-finding methods, futility screening, treatment selection, and early stopping rules. These methods are often proposed to address the conventional cytotoxic therapeutics for neoplastic diseases and cancer. Recent advances in precision medicine have motivated novel trial designs, most notably the idea of master protocol (eg, platform trial, basket trial, umbrella trial, N-of-1 trial), for the evaluation of molecularly targeted cancer therapies. In this article, we review the concepts and methodology of early-phase cancer trial designs with a focus on dose finding and treatment screening and put these methods in the context of platform trials of molecularly targeted cancer therapies. Because most cancer trial designs have been developed for cytotoxic agents, we will discuss how these time-tested design principles hold relevance for targeted cancer therapies, and we will delineate how a master protocol may serve as an efficient platform for safety and efficacy evaluations of novel targeted therapies.

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The i3+3 design for phase I clinical trials.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.3066 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 3066-3066

Author(s):

Yuan Ji ◽

Meizi Liu

Keyword(s):

Phase I ◽

Dose Finding ◽

Operating Characteristics ◽

Phase I Clinical Trials ◽

Rule Based ◽

Model Based ◽

Simple Rules ◽

R Shiny ◽

Statistical Designs ◽

Immune Oncology

3066 Background: Other than the 3+3 design, new model-based statistical designs like the mTPI design (Ji and Wang, 2013, JCO) are alternative choices for oncology dose-finding trials, including immune oncology dose-finding trials (Atkins et al., 2018, Lancet Oncology). One major criticism of the 3+3 design is that it is based on simple rules, does not depend on statistical models for inference, and leads to unsafe and unreliable operating characteristics. However, the rule-based nature allows 3+3 to be easily understood and implemented in practice, making it practically attractive and friendly. Can friendly rule-based designs achieve great performance seen in model-based designs? For four decades, the answer has been NO. Methods: We propose a new rule-based design called i3+3, where the letter "i" represents the word "interval". The i3+3 design is based on simple but more clever rules that account for the variabilities in the observed data. In short, the i3+3 design simply asks clinicians to compare observed toxicity rates with a prespecified toxicity interval, and make dose escalation decisions according to three simple rules. No sophisticated modeling is needed and the entire design is transparent to clinicians. Results: We compare the operating characteristics for the proposed i3+3 design with other popular phase I designs by simulation. The i3+3 design is far superior than the 3+3 design in trial safety and the ability to identify the true MTD. Compared with model-based phase I designs, i3+3 also demonstrates comparable performances. In other words, the i3+3 design possesses both simplicity and transparency of the rule-based approaches, and the superior operating characteristics seen in model-based approaches. An online R Shiny tool is provided to illustrate the i3+3 design, although in practice it requires no software to design or conduct a dose-finding trial using the design. Conclusions: The i3+3 design could be a practice-altering method for the clinical community. It may increase the safety and efficiency of dose finding trials.

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An adaptive design for the identification of the optimal dose using joint modeling of continuous repeated biomarker measurements and time-to-toxicity in phase I/II clinical trials in oncology

Statistical Methods in Medical Research ◽

10.1177/0962280219837737 ◽

2019 ◽

Vol 29 (2) ◽

pp. 508-521 ◽

Cited By ~ 1

Author(s):

Maria-Athina Altzerinakou ◽

Xavier Paoletti

Keyword(s):

Phase I ◽

Adaptive Design ◽

Random Effect ◽

Joint Modeling ◽

Maximum Tolerated Dose ◽

Small Sample ◽

Dose Finding ◽

Operating Characteristics ◽

Activity Measurements ◽

Dose Limiting Toxicity

We present a new adaptive dose-finding method, based on a joint modeling of longitudinal continuous biomarker activity measurements and time to first dose limiting toxicity, with a shared random effect. Estimation relies on likelihood that does not require approximation, an important property in the context of small sample sizes, typical of phase I/II trials. We address the important case of missing at random data that stem from unacceptable toxicity, lack of activity and rapid deterioration of phase I patients. The objective is to determine the lowest dose within a range of highly active doses, under the constraint of not exceeding the maximum tolerated dose. The maximum tolerated dose is associated to some cumulative risk of dose limiting toxicity over a predefined number of treatment cycles. Operating characteristics are explored via simulations in various scenarios.

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Probability intervals of toxicity and efficacy design for dose-finding clinical trials in oncology

Statistical Methods in Medical Research ◽

10.1177/0962280220977009 ◽

2020 ◽

pp. 096228022097700

Author(s):

Xiaolei Lin ◽

Yuan Ji

Keyword(s):

Adoptive Cell Therapy ◽

Dose Finding ◽

Marginal Model ◽

Therapeutic Effects ◽

Operating Characteristics ◽

Cell Therapies ◽

Previous Design ◽

Statistical Designs ◽

Improved Design ◽

Finding Method

Immunotherapy, gene therapy or adoptive cell therapies, such as the chimeric antigen receptor+ T-cell therapies, have demonstrated promising therapeutic effects in oncology patients. We consider statistical designs for dose-finding adoptive cell therapy trials, in which the monotonic dose–response relationship assumed in traditional oncology trials may not hold. Building upon a previous design called “TEPI”, we propose a new dose finding method – Probability Intervals of Toxicity and Efficacy (PRINTE), which utilizes toxicity and efficacy jointly in making dosing decisions, does not require a pre-elicited decision table and at the same time can handle Ockham’s razor properly in the statistical inference. We show that optimizing the joint posterior expected utility of toxicity and efficacy under a 0–1 loss is equivalent to maximizing the marginal model posterior probability in the two-dimensional probability space. An extensive simulation study under various scenarios are conducted and results show that PRINTE outperforms existing designs in the literature since it assigns more patients to optimal doses and less to toxic ones, and selects optimal doses with higher percentages. The simple and transparent features together with good operating characteristics make PRINTE an improved design for dose-finding trials in oncology trials.

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A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades

Stats ◽

10.3390/stats3030017 ◽

2020 ◽

Vol 3 (3) ◽

pp. 221-238

Author(s):

Márcio A. Diniz ◽

Sungjin Kim ◽

Mourad Tighiouart

Keyword(s):

Adaptive Design ◽

Parametric Models ◽

Model Parameters ◽

Operating Characteristics ◽

Bayesian Adaptive Design ◽

Continual Reassessment ◽

Proportional Odds Assumption ◽

Dose Combination ◽

The One ◽

The Relationship

We propose a Bayesian adaptive design for early phase drug combination cancer trials incorporating ordinal grade of toxicities. Parametric models are used to describe the relationship between the dose combinations and the probabilities of the ordinal toxicities under the proportional odds assumption. Trial design proceeds by treating cohorts of two patients simultaneously receiving different dose combinations. Specifically, at each stage of the trial, we seek the dose of one agent by minimizing the Bayes risk with respect to a loss function given the current dose of the other agent. We consider two types of loss functions corresponding to the Continual Reassessment Method (CRM) and Escalation with Overdose Control (EWOC). At the end of the trial, we estimate the MTD curve as a function of Bayes estimates of the model parameters. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD by comparing this design to the one that uses a binary indicator of DLT. The methodology is further adapted to the case of a pre-specified discrete set of dose combinations.

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