A robust Bayesian dose-finding design for phase I/II clinical trials

Liu, Su-Yu; Johnson, Valen E.

doi:10.1093/biostatistics/kxv040

Cited by 60 publications

(102 citation statements)

References 29 publications

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“…Liu and Johnson proposed another marginal‐probability‐based trade‐off function

U_{j}^{M 2} = π_{E, j} - w_{1} π_{T, j} - w_{2} π_{T, j} I false(π_{T, j} > ρ false),

where w 1 and w 2 are prespecified weights, I (·) is an indicator function, and ρ is a prespecified toxicity threshold deemed of substantial concern. Compared to

U_{j}^{M}

in , this trade‐off function is more flexible and allows to impose a higher penalty (ie, w 1 + w 2 ) when the true DLT rate π T , j exceeds the threshold ρ .…”

Section: Methodsmentioning

confidence: 99%

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A utility‐based Bayesian optimal interval (U‐BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies

Zhou

Lee

Yuan

2019

Statistics in Medicine

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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. We develop a utility‐based Bayesian optimal interval (U‐BOIN) phase I/II design to find the OBD. 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 seamless stages. In stage I, the Bayesian optimal interval design is used to quickly explore the dose space and collect preliminary toxicity and efficacy data. In stage II, we continuously update the posterior estimate of the utility for each dose after each cohort, using accumulating efficacy and toxicity from both stages I and II, and then use the posterior estimate 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. Our simulation study shows that, despite its simplicity, the U‐BOIN design is robust and has high accuracy to identify the OBD. We extend the design to accommodate delayed efficacy by leveraging the short‐term endpoint (eg, immune activity or other biological activity of targeted agents), and using it to predict the delayed efficacy outcome to facilitate real‐time decision making. A user‐friendly software to implement the U‐BOIN is freely available at www.trialdesign.org.

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“…Liu and Johnson proposed another marginal‐probability‐based trade‐off function

U_{j}^{M 2} = π_{E, j} - w_{1} π_{T, j} - w_{2} π_{T, j} I false(π_{T, j} > ρ false),

where w 1 and w 2 are prespecified weights, I (·) is an indicator function, and ρ is a prespecified toxicity threshold deemed of substantial concern. Compared to

U_{j}^{M}

in , this trade‐off function is more flexible and allows to impose a higher penalty (ie, w 1 + w 2 ) when the true DLT rate π T , j exceeds the threshold ρ .…”

Section: Methodsmentioning

confidence: 99%

“…Jin et al proposed a phase I/II design that accommodates late‐onset toxicity and efficacy. Liu and Johnson proposed a robust Bayesian phase I/II design, based on a flexible Bayesian dynamic model. Guo and Yuan proposed a personalized Bayesian phase I/II design that accounts for patient characteristics and biomarker information.…”

Section: Introductionmentioning

confidence: 99%

A utility‐based Bayesian optimal interval (U‐BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies

Zhou

Lee

Yuan

2019

Statistics in Medicine

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“…Following Liu and Johnson, we define an utility function for OBD selection,

U ({\tilde{p}}_{j}, {\tilde{q}}_{j}) = {\tilde{q}}_{j} - w_{1} {\tilde{p}}_{j} - w_{2} {\tilde{p}}_{j} I {{\tilde{p}}_{j} > ϕ_{0}},

where w 1 ⩾0 and w 2 ⩾0 are nonnegative weights representing the importance of toxicity and efficacy outcomes. Such a utility function is flexible, and it can meet various requirements from clinicians by varying the weights.…”

Section: A Stein Designmentioning

confidence: 99%

“…For example, if w 1 =0 and

w_{2} = \infty

, the tolerated dose level with the maximum efficacious effect is preferred; if

0 < w_{1}, w_{2} ⩽ \infty

, the dose level that possesses a desirable trade‐off between toxicity and efficacy is selected. For details on the selection of w 1 and w 2 , see Liu and Johnson . The OBD is determined as the dose level that has the maximum utility

j^{*} = \underset{j \in N}{\arg \max} \{U ({\tilde{p}}_{j}, {\tilde{q}}_{j})\},

where

scriptN

contains all the dose levels that have been tried during the trial.…”

Section: A Stein Designmentioning

confidence: 99%

“…To investigate the performance of the proposed method, we compare the operating characteristics of the STEIN design with the L‐logistic method, the B‐dynamic method, and the dose‐finding method for molecularly targeted agent using randomization (MTA‐RA) via a simulation study. The B‐dynamic method requires the monotone dose‐response relationships, and the MTA‐RA method is focused on the case when efficacy increases initially with the dose and then plateaus.…”

Section: Simulation Studymentioning

confidence: 99%

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STEIN: A simple toxicity and efficacy interval design for seamless phase I/II clinical trials

Lin

Yin

2017

Statistics in Medicine

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Seamless phase I/II dose-finding trials are attracting increasing attention nowadays in early-phase drug development for oncology. Most existing phase I/II dose-finding methods use sophisticated yet untestable models to quantify dose-toxicity and dose-efficacy relationships, which always renders them difficult to implement in practice. To simplify the practical implementation, we extend the Bayesian optimal interval design from maximum tolerated dose finding to optimal biological dose finding in phase I/II trials. In particular, optimized intervals for toxicity and efficacy are respectively derived by minimizing probabilities of incorrect classifications. If the pair of observed toxicity and efficacy probabilities at the current dose is located inside the promising region, we retain the current dose; if the observed probabilities are outside of the promising region, we propose an allocation rule by maximizing the posterior probability that the response rate of the next dose falls inside a prespecified efficacy probability interval while still controlling the level of toxicity. The proposed interval design is model-free, thus is suitable for various dose-response relationships. We conduct extensive simulation studies to demonstrate the small- and large-sample performance of the proposed method under various scenarios. Compared to existing phase I/II dose-finding designs, not only is our interval design easy to implement in practice, but it also possesses desirable and robust operating characteristics.

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Bayesian adaptive dose‐escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

Yeung

Reigner

Beyer

et al. 2017

Pharmaceutical Statistics

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The main purpose of dose-escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose-escalation designs that incorporate both the dose-limiting events and dose-limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose-escalation strategies. The first type of procedures, called "single objective," aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called "dual objective," aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose-escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual-objective designs give better results in terms of identifying the 2 real target doses compared to the single-objective designs.

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A robust Bayesian dose-finding design for phase I/II clinical trials

Cited by 60 publications

References 29 publications

A utility‐based Bayesian optimal interval (U‐BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies

A utility‐based Bayesian optimal interval (U‐BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies

STEIN: A simple toxicity and efficacy interval design for seamless phase I/II clinical trials

Bayesian adaptive dose‐escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

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