An adaptive dose-finding approach for correlated bivariate binary and continuous outcomes in phase I oncology trials

In this study, we developed a novel adaptive dose-finding approach for inclusion of correlated bivariate binary and continuous outcomes in designing phase I oncology trials. For this approach, binary toxicity and continuous efficacy outcomes are modeled jointly with a factorization model. The basic strategy of the proposed approach is based primarily on the Bayesian method. We based the dose escalation/de-escalation decision rules on the posterior distributions of both toxicity and efficacy outcomes. We compared the operating characteristics of the proposed and existing methods through simulation studies under various scenarios. We found that the recommendation rate of the true recommended dose (RD) in the proposed method was more favorable than that in the existing method when the true RD was relatively at the tail end among the tested doses. It was similar to that of the existing method when the true RD was relatively at the top end.     

A Bayesian adaptive phase I/II platform trial design for pediatric immunotherapy trials

Immunotherapy is the most promising new cancer treatment for various pediatric tumors and has resulted in an unprecedented surge in the number of novel immunotherapeutic treatments that need to be evaluated in clinical trials. Most phase I/II trial designs have been developed for evaluating only one candidate treatment at a time, and are thus not optimal for this task. To address these issues, we propose a Bayesian phase I/II platform trial design, which accounts for the unique features of immunotherapy, thereby allowing investigators to continuously screen a large number of immunotherapeutic treatments in an efficient and seamless manner. The elicited numerical utility is adopted to account for the risk‐benefit trade‐off and to quantify the desirability of the dose. During the trial, inefficacious or overly toxic treatments are adaptively dropped from the trial and the promising treatments are graduated from the trial to the next stage of development. Once an experimental treatment is dropped or graduated, the next available new treatment can be immediately added and tested. Extensive simulation studies have demonstrated the desirable operating characteristics of the proposed design.     

A Bayesian dose finding design for dual endpoint phase I trials

We propose a dose-finding weighted design for an early clinical trial which aims to determine the optimal dose, selected on the basis of both efficacy and toxicity, to be used in patients entering subsequent studies in a drug development process. The goal is to identify the optimal dose, while using a minimal number of subjects. For each dose under test, a decision table is defined with a utility value attached to each possible decision. The relationship between the utility and the target probability for each outcome is shown. A Dirichlet prior is used and we illustrate the process of maximizing the expected utility under the resulting posterior distribution to find the optimal decision at each stage of the trial. We show how this affects the eventual choice of optimal dose in various scenarios. Properties of our design are discussed and compared with a current standard design.     

Testing of evaluation bias for progression free survival endpoint in oncology clinical trials

Progression‐free survival is an increasingly popular end point in oncology clinical trials. A complete blinded independent central review (BICR) is often required by regulators in an attempt to reduce the bias in progression‐free survival (PFS) assessment. In this paper, we propose a new methodology that uses a sample‐based BICR as an audit tool to decide whether a complete BICR is needed. More specifically, we propose a new index, the differential risk, to measure the reading discordance pattern, and develop a corresponding hypothesis testing procedure to decide whether the bias in local evaluation is acceptable. Simulation results demonstrate that our new index is sensitive to the change of discordance pattern; type I error is well controlled in the hypothesis testing procedure, and the calculated sample size provides the desired power. Copyright © 2016 John Wiley & Sons, Ltd.     

A Bayesian predictive two-stage design for phase II clinical trials

In this paper, we propose a Bayesian two-stage design for phase II clinical trials, which represents a predictive version of the single threshold design (STD) recently introduced by Tan and Machin. The STD two-stage sample sizes are determined specifying a minimum threshold for the posterior probability that the true response rate exceeds a pre-specified target value and assuming that the observed response rate is slightly higher than the target. Unlike the STD, we do not refer to a fixed experimental outcome, but take into account the uncertainty about future data. In both stages, the design aims to control the probability of getting a large posterior probability that the true response rate exceeds the target value. Such a probability is expressed in terms of prior predictive distributions of the data. The performance of the design is based on the distinction between analysis and design priors, recently introduced in the literature. The properties of the method are studied when all the design parameters vary.     

Incorporating patient-reported outcomes in dose-finding clinical trials

Oncology dose-finding clinical trials determine the maximum tolerated dose (MTD) based on toxicity outcomes captured by clinicians. With the availability of more rigorous instruments for measuring toxicity directly from patients, there is a growing interest to incorporate patient-reported outcomes (PRO) in clinical trials to inform patient tolerability. This is particularly important for dose-finding trials to ensure the identification of a well-tolerated dose. In this paper, we propose three extensions of the continual reassessment method (CRM), termed PRO-CRMs, that incorporate both clinician and patient outcomes. The first method is a marginal modeling approach whereby clinician and patient toxicity outcomes are modeled separately. The other two methods impose a constraint using a joint outcome defined based on both clinician and patient toxicities and model them either jointly or marginally. Simulation studies show that while all three PRO-CRMs select well-tolerated doses based on clinician's and patient's perspectives, the methods using a joint outcome perform better and have similar performance. We also show that the proposed PRO-CRMs are consistent under robust model assumptions.     

Bayesian methods for analysis of biosimilar phase III trials

A biologic is a product made from living organisms. A biosimilar is a new version of an already approved branded biologic. Regulatory guidelines recommend a totality‐of‐the‐evidence approach with stepwise development for a new biosimilar. Initial steps for biosimilar development are (a) analytical comparisons to establish similarity in structure and function followed by (b) potential animal studies and a human pharmacokinetics/pharmacodynamics equivalence study. The last step is a phase III clinical trial to confirm similar efficacy, safety, and immunogenicity between the biosimilar and the biologic. A high degree of analytical and pharmacokinetics/pharmacodynamics similarity could provide justification for an eased statistical threshold in the phase III trial, which could then further facilitate an overall abbreviated approval process for biosimilars. Bayesian methods can help in the analysis of clinical trials, by adding proper prior information into the analysis, thereby potentially decreasing required sample size. We develop proper prior information for the analysis of a phase III trial for showing that a proposed biosimilar is similar to a reference biologic. For the reference product, we use a meta‐analysis of published results to set a prior for the probability of efficacy, and we propose priors for the proposed biosimilar informed by the strength of the evidence generated in the earlier steps of the approval process. A simulation study shows that with few exceptions, the Bayesian relative risk analysis provides greater power, shorter 90% credible intervals with more than 90% frequentist coverage, and better root mean squared error.     

A model-based approach in the estimation of the maximum tolerated dose in phase I cancer clinical trials

The primary aim of a phase I cancer clinical trial is to determine the maximum tolerated dose (MTD) of a new agent. The MTD is determined as the highest dose level of a potential therapeutic agent at which the patients have experienced an acceptable level of dose limiting toxicity. Although many other types of designs have been proposed in recent years, the traditional algorithm-based designs, especially the 3+3 designs, are still widely used due to their practical simplicity. Simulation studies have shown that the traditional algorithm-based designs cannot provide reasonable estimates of the MTD due to their intrinsic design limitations. In this paper, we propose a model-based approach in the estimation of the MTD following a traditional 3+3 design. Simulation results indicate that our model-based approach produces much less biased estimates of the MTD compared to the estimates obtained from the traditional 3+3 designs. Furthermore, our model-based approach can be easily extended to any traditional A+B design.     

Critical aspects of the Bayesian approach to phase I cancer trials

The Bayesian approach to finding the maximum-tolerated dose in phase I cancer trials is discussed. The suggested approach relies on a realistic dose-toxicity model, allows one to include prior information, and supports clinical decision making by presenting within-trial information in a transparent way. The modeling and decision-making components are flexible enough to be extendable to more complex settings. Critical aspects are emphasized and a comparison with the continual reassessment method (CRM) is performed with data from an actual trial and a simulation study. The comparison revealed similar operating characteristics while avoiding some of the difficulties encountered in the actual trial when applying the CRM.     

A Bayesian dose‐finding procedure for phase I clinical trials based only on the assumption of monotonicity

Despite an enormous and growing statistical literature, formal procedures for dose‐finding are only slowly being implemented in phase I clinical trials. Even in oncology and other life‐threatening conditions in which a balance between efficacy and toxicity has to be struck, model‐based approaches, such as the Continual Reassessment Method, have not been universally adopted. Two related concerns have limited the adoption of the new methods. One relates to doubts about the appropriateness of models assumed to link the risk of toxicity to dose, and the other is the difficulty of communicating the nature of the process to clinical investigators responsible for early phase studies. In this paper, we adopt a new Bayesian approach involving a simple model assuming only monotonicity in the dose‐toxicity relationship. The parameters that define the model have immediate and simple interpretation. The approach can be applied automatically, and we present a simulation investigation of its properties when it is. More importantly, it can be used in a transparent fashion as one element in the expert consideration of what dose to administer to the next patient or group of patients. The procedure serves to summarize the opinions and the data concerning risks of a binary characterization of toxicity which can then be considered, together with additional and less tidy trial information, by the clinicians responsible for making decisions on the allocation of doses. Graphical displays of these opinions can be used to ease communication with investigators. Copyright © 2010 John Wiley & Sons, Ltd.     

Bayesian decision theoretic two-stage design in phase II clinical trials with survival endpoint

In this paper, we consider two-stage designs with failure-time endpoints in single-arm phase II trials. We propose designs in which stopping rules are constructed by comparing the Bayes risk of stopping at stage I with the expected Bayes risk of continuing to stage II using both the observed data in stage I and the predicted survival data in stage II. Terminal decision rules are constructed by comparing the posterior expected loss of a rejection decision versus an acceptance decision. Simple threshold loss functions are applied to time-to-event data modeled either parametrically or nonparametrically, and the cost parameters in the loss structure are calibrated to obtain desired type I error and power. We ran simulation studies to evaluate design properties including types I and II errors, probability of early stopping, expected sample size, and expected trial duration and compared them with the Simon two-stage designs and a design, which is an extension of the Simon's designs with time-to-event endpoints. An example based on a recently conducted phase II sarcoma trial illustrates the method.     

A Bayesian design for phase I cancer therapeutic vaccine trials

Phase I clinical trials are the first step in drug development to test a new drug or drug combination on humans. Typical designs of Phase I trials use toxicity as the primary endpoint and aim to find the maximum tolerable dosage. However, these designs are poorly applicable for the development of cancer therapeutic vaccines because the expected safety concerns for these vaccines are not as much as cytotoxic agents. The primary objectives of a cancer therapeutic vaccine phase I trial thus often include determining whether the vaccine shows biologic activity and the minimum dose necessary to achieve a full immune or even clinical response. In this paper, we propose a new Bayesian phase I trial design that allows simultaneous evaluation of safety and immunogenicity outcomes. We demonstrate the proposed clinical trial design by both a numeric study and a therapeutic human papillomavirus vaccine trial.     

Bayesian two-stage designs for phase II clinical trials

Many different statistical designs have been used in phase II clinical trials. The majority of these are based on frequentist statistical approaches. Bayesian methods provide a good alternative to frequentist approaches as they allow for the incorporation of relevant prior information and the presentation of the trial results in a manner which, some feel, is more intuitive and helpful. In this paper, we propose two new Bayesian designs for phase II clinical trials. These designs have been developed specifically to make them as user friendly and as familiar as possible to those who have had experience working with two-stage frequentist phase II designs. Thus, unlike many of the Bayesian designs already proposed in the literature, our designs do not require a distribution for the response rate of the currently used drug or the explicit specification of utility or loss functions. We study the properties of our designs and compare them with the Simon two-stage optimal and minimax designs. We also apply them to an example of two recently concluded phase II trials conducted at the National Cancer Centre in Singapore. Sample size tables for the designs are given.     

Calibrated phase II clinical trials in oncology

This paper proposes the use of calibrated designs in phase II oncological clinical trials and evaluates their statistical properties in terms of power recovery and cost. A calibrated phase II design for a new cancer treatment for a specific tumour, e.g. colo-rectal, consists of random allocation of patients to receive either the investigational treatment or a standard treatment known to have activity at a certain level in phase II trials (e.g. 5 FU, expected response proportion = 0.20). Patients assigned to the standard treatment form the calibration group. The calibration group is not a control group in the traditional sense and one does not conduct a formal efficacy comparison between the investigational treatment group and the calibration group. Instead, one uses the calibration group to evaluate whether the sample of patients who receive the investigational treatment has the capability of showing a response. If the data do not support the hypothesis that the expected response proportion prevails in the calibration group, one declares the investigational group results suspect and recommends a second trial. Assuming acceptable results of the second trial, we use binomial calculations to find the effect of the calibration design on power recovery and relative cost. We show that when an unrepresentative sample occurs, calibration designs generally recover 90 per cent or more of nominal power at a cost of three to fivefold increase in sample size. We recommend for calibrated phase II trials a 'master protocol' approach in which several investigational treatment arms share one concurrent calibration group.     

A Bayesian dose‐finding design for phase I/II clinical trials with nonignorable dropouts

Phase I/II trials utilize both toxicity and efficacy data to achieve efficient dose finding. However, due to the requirement of assessing efficacy outcome, which often takes a long period of time to be evaluated, the duration of phase I/II trials is often longer than that of the conventional dose‐finding trials. As a result, phase I/II trials are susceptible to the missing data problem caused by patient dropout, and the missing efficacy outcomes are often nonignorable in the sense that patients who do not experience treatment efficacy are more likely to drop out of the trial. We propose a Bayesian phase I/II trial design to accommodate nonignorable dropouts. We treat toxicity as a binary outcome and efficacy as a time‐to‐event outcome. We model the marginal distribution of toxicity using a logistic regression and jointly model the times to efficacy and dropout using proportional hazard models to adjust for nonignorable dropouts. The correlation between times to efficacy and dropout is modeled using a shared frailty. We propose a two‐stage dose‐finding algorithm to adaptively assign patients to desirable doses. Simulation studies show that the proposed design has desirable operating characteristics. Our design selects the target dose with a high probability and assigns most patients to the target dose. Copyright © 2015 John Wiley & Sons, Ltd.     

BOP2: Bayesian optimal design for phase II clinical trials with simple and complex endpoints

We propose a flexible Bayesian optimal phase II (BOP2) design that is capable of handling simple (e.g., binary) and complicated (e.g., ordinal, nested, and co‐primary) endpoints under a unified framework. We use a Dirichlet‐multinomial model to accommodate different types of endpoints. At each interim, the go/no‐go decision is made by evaluating a set of posterior probabilities of the events of interest, which is optimized to maximize power or minimize the number of patients under the null hypothesis. Unlike other existing Bayesian designs, the BOP2 design explicitly controls the type I error rate, thereby bridging the gap between Bayesian designs and frequentist designs. In addition, the stopping boundary of the BOP2 design can be enumerated prior to the onset of the trial. These features make the BOP2 design accessible to a wide range of users and regulatory agencies and particularly easy to implement in practice. Simulation studies show that the BOP2 design has favorable operating characteristics with higher power and lower risk of incorrectly terminating the trial than some existing Bayesian phase II designs. The software to implement the BOP2 design is freely available at www.trialdesign.org . Copyright © 2017 John Wiley & Sons, Ltd.     

Bayesian enhancement two-stage design with error control for phase II clinical trials

Phase II clinical trials make a critical decision of go or no-go to a subsequent phase III studies. A considerable proportion of promising drugs identified in phase II trials fail the confirmative efficacy test in phase III. Recognizing the low posterior probabilities of H₁ when accepting the drug under Simon's two-stage design, the Bayesian enhancement two-stage (BET) design is proposed to strengthen the passing criterion. Under the BET design, the lengths of highest posterior density (HPD) intervals, posterior probabilities of H₀ and H₁ are computed to calibrate the design parameters, aiming to improve the stability of the trial characteristics and strengthen the evidence for proceeding the drug development forward. However, from a practical perspective, the HPD interval length lacks transparency and interpretability. To circumvent this problem, we propose the BET design with error control (BETEC) by replacing the HPD interval length with the posterior error rate. The BETEC design can achieve a balance between the posterior false positive rate and false negative rate and, more importantly, it has an intuitive and clear interpretation. We compare our method with the BET design and Simon's design through extensive simulation studies. As an illustration, we further apply BETEC to two recent clinical trials, and investigate its performance in comparison with other competitive designs. Being both efficient and intuitive, the BETEC design can serve as an alternative toolbox for implementing phase II single-arm trials.     

A Bayesian predictive strategy for an adaptive two‐stage design in phase II clinical trials

Phase II clinical trials are typically designed as two‐stage studies, in order to ensure early termination of the trial if the interim results show that the treatment is ineffective. Most of two‐stage designs, developed under both a frequentist and a Bayesian framework, select the second stage sample size before observing the first stage data. This may cause some paradoxical situations during the practical carrying out of the trial. To avoid these potential problems, we suggest a Bayesian predictive strategy to derive an adaptive two‐stage design, where the second stage sample size is not selected in advance, but depends on the first stage result. The criterion we propose is based on a modification of a Bayesian predictive design recently presented in the literature (see (Statist. Med. 2008; 27 :1199–1224)). The distinction between analysis and design priors is essential for the practical implementation of the procedure: some guidelines for choosing these prior distributions are discussed and their impact on the required sample size is examined. Copyright © 2010 John Wiley & Sons, Ltd.     

Bayesian procedures for phase I/II clinical trials investigating the safety and efficacy of drug combinations

Many formal statistical procedures for phase I dose-finding studies have been proposed. Most concern a single novel agent available at a number of doses and administered to subjects participating in a single treatment period and returning a single binary indicator of toxicity. Such a structure is common when evaluating cytotoxic drugs for cancer. This paper concerns studies of combinations of two agents, both available at several doses. Subjects participate in one treatment period and provide two binary responses: one an indicator of benefit and the other of harm. The word 'benefit' is used loosely here: the response might be an early indicator of physiological change which, if induced in patients, is of potential therapeutic value. The context need not be oncology, but might be any study intended to meet both the phase I aim of establishing which doses are safe and the phase II goal of exploring potential therapeutic activity. A Bayesian approach is used based on an assumption of monotonicity in the relationship between the strength of the dose-combination and the distribution of the bivariate outcome. Special cases are described, and the procedure is evaluated using simulation. The parameters that define the model have immediate and simple interpretation. Graphical representations of the posterior opinions about model parameters are shown, and these can be used to inform the discussions of the trial safety committee.     

A three-outcome design for randomized comparative phase II clinical trials

Randomized designs have been increasingly called for use in phase II oncology clinical trials to protect against potential patient selection bias. However, formal statistical comparison is rarely conducted due to the sample size restriction, despite its appeal. In this paper, we offer an approach to sample size reduction by extending the three-outcome design of Sargent et al. (Control Clin. Trials 2001; 22:117-125) for single-arm trials to randomized comparative trials. In addition to the usual two outcomes of a hypothesis testing (rejecting the null hypothesis or rejecting the alternative hypothesis), the three-outcome comparative design allows a third outcome of rejecting neither hypotheses when the testing result is in some 'grey area' and leaves the decision to the clinical judgment based on the overall evaluation of trial outcomes and other relevant factors. By allowing a reasonable region of uncertainty, the three-outcome design enables formal statistical comparison with considerably smaller sample size, compared to the standard two-outcome comparative design. Statistical formulation of the three-outcome comparative design is discussed for both the single-stage and two-stage trials. Sample sizes are tabulated for some common clinical scenarios.