Flexible designs for phase II comparative clinical trials involving two response variables

The aim of phase II clinical trials is to determine whether an experimental treatment is sufficiently promising and safe to justify further testing. The need for reduced sample size arises naturally in phase II clinical trials owing to both technical and ethical reasons, motivating a significant part of research in the field during recent years, while another significant part of the research effort is aimed at more complex therapeutic schemes that demand the consideration of multiple endpoints to make decisions. In this paper, our attention is restricted to phase II clinical trials in which two treatments are compared with respect to two dependent dichotomous responses proposing some flexible designs. These designs permit the researcher to terminate the clinical trial when high rates of favorable or unfavorable outcomes are observed early enough requiring in this way a small number of patients. From the mathematical point of view, the proposed designs are defined on bivariate sequences of multi‐state trials, and the corresponding stopping rules are based on various distributions related to the waiting time until a certain number of events appear in these sequences. The exact distributions of interest, under a unified framework, are studied using the Markov chain embedding technique, which appears to be very useful in clinical trials for the sample size determination. Tables of expected sample size and power are presented. The numerical illustration showed a very good performance for these new designs. Copyright © 2014 John Wiley & Sons, Ltd.     

A new approach to designing phase I‐II cancer trials for cytotoxic chemotherapies

Recently, there has been much work on early phase cancer designs that incorporate both toxicity and efficacy data, called phase I‐II designs because they combine elements of both phases. However, they do not explicitly address the phase II hypothesis test of H₀ : p ? p₀, where p is the probability of efficacy at the estimated maximum tolerated dose from phase I and p₀ is the baseline efficacy rate. Standard practice for phase II remains to treat p as a fixed, unknown parameter and to use Simon's two‐stage design with all patients dosed at . We propose a phase I‐II design that addresses the uncertainty in the estimate in H₀ by using sequential generalized likelihood theory. Combining this with a phase I design that incorporates efficacy data, the phase I‐II design provides a common framework that can be used all the way from the first dose of phase I through the final accept/reject decision about H₀ at the end of phase II, utilizing both toxicity and efficacy data throughout. Efficient group sequential testing is used in phase II that allows for early stopping to show treatment effect or futility. The proposed phase I‐II design thus removes the artificial barrier between phase I and phase II and fulfills the objectives of searching for the maximum tolerated dose and testing if the treatment has an acceptable response rate to enter into a phase III trial. Copyright © 2014 John Wiley & Sons, Ltd.     

A robust two‐stage design identifying the optimal biological dose for phase I/II clinical trials

We propose a robust two‐stage design to identify the optimal biological dose for phase I/II clinical trials evaluating both toxicity and efficacy outcomes. In the first stage of dose finding, we use the Bayesian model averaging continual reassessment method to monitor the toxicity outcomes and adopt an isotonic regression method based on the efficacy outcomes to guide dose escalation. When the first stage ends, we use the Dirichlet‐multinomial distribution to jointly model the toxicity and efficacy outcomes and pick the candidate doses based on a three‐dimensional volume ratio. The selected candidate doses are then seamlessly advanced to the second stage for dose validation. Both toxicity and efficacy outcomes are continuously monitored so that any overly toxic and/or less efficacious dose can be dropped from the study as the trial continues. When the phase I/II trial ends, we select the optimal biological dose as the dose obtaining the minimal value of the volume ratio within the candidate set. An advantage of the proposed design is that it does not impose a monotonically increasing assumption on the shape of the dose–efficacy curve. We conduct extensive simulation studies to examine the operating characteristics of the proposed design. The simulation results show that the proposed design has desirable operating characteristics across different shapes of the underlying true dose–toxicity and dose–efficacy curves. The software to implement the proposed design is available upon request. Copyright © 2016 John Wiley & Sons, Ltd.     

Adaptive dose modification for phase I clinical trials

Most phase I dose‐finding methods in oncology aim to find the maximum‐tolerated dose from a set of prespecified doses. However, in practice, because of a lack of understanding of the true dose–toxicity relationship, it is likely that none of these prespecified doses are equal or reasonably close to the true maximum‐tolerated dose. To handle this issue, we propose an adaptive dose modification (ADM) method that can be coupled with any existing dose‐finding method to adaptively modify the dose, when it is needed, during the course of dose finding. To reflect clinical practice, we divide the toxicity probability into three regions: underdosing, acceptable, and overdosing regions. We adaptively add a new dose whenever the observed data suggest that none of the investigational doses are likely to be located in the acceptable region. The new dose is estimated via a nonparametric dose–toxicity model based on local polynomial regression. The simulation study shows that ADM substantially outperforms the similar existing method. We applied ADM to a phase I cancer trial. Copyright © 2016 John Wiley & Sons, Ltd.     

Conditionally unbiased estimation in phase II/III clinical trials with early stopping for futility

Seamless phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages, with stage 1 used to answer phase II objectives such as treatment selection and stage 2 used for the confirmatory analysis, which is a phase III objective. Although seamless phase II/III clinical trials are efficient because the confirmatory analysis includes phase II data from stage 1, inference can pose statistical challenges. In this paper, we consider point estimation following seamless phase II/III clinical trials in which stage 1 is used to select the most effective experimental treatment and to decide if, compared with a control, the trial should stop at stage 1 for futility. If the trial is not stopped, then the phase III confirmatory part of the trial involves evaluation of the selected most effective experimental treatment and the control. We have developed two new estimators for the treatment difference between these two treatments with the aim of reducing bias conditional on the treatment selection made and on the fact that the trial continues to stage 2. We have demonstrated the properties of these estimators using simulations. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive dose finding for phase I clinical trials of drugs used for chemotherapy of cancer

Phase I clinical trials of cancer chemotherapy drugs are intended to determine the maximum tolerable dose (MTD). Thestandard method employed is a rule-based dose-escalation scheme in which escalation depends on the number of patients at a dose level that have dose-limiting toxicity (DLT). The MTD is thus defined in terms of the rules and a series of dose levels selected for sampling. For some trials it is desirable to have a more precise definition of the MTD, and to determine the MTD more accurately than possible with the rule-based schemes. Continuous reassessment methods (CRMs) define the MTD to be the dose at which a fixed fraction of patients experience DLT, and thus appear suited to these trials. It is shown, however, that these methods can have failure modes that in fact make them unattractive. An alternative data-driven dose-finding method is described that combines the robustness of the rule-based methods and with features of CRMs. The method has two stages. In the first stage, doses are escalated by a factor of 1.5. In the second stage, which begins at the first instance of DLT, a two-parameter logistic dose-response model estimates the MTD from the DLT experience of all patients. The model is initialized by setting the dose (d10) at which 10 per cent of patients would experience DLT to half the dose at which the first DLT was observed, and the dose (d90) at which 90 per cent would experience DLT to ten times d10. Weights are assigned such that the information at d10 and d90 is equivalent to that of one patient at each of the two doses. Cohorts of three patients are treated in both stages, and the dose for a new cohort in the second stage is the estimated MTD. The only prior information required to specify the design completely is the dose which will be given to the first cohort. Two stopping rules are investigated; among the requirements for these are that at least three (or four) DLTs be observed and at least nine patients be treated in the second stage. Simulations show that a coefficient of variation of approximately 0.4 on a target DLT probability of 0.3 is obtained over a wide variation in dose-response characteristics of the study drug. The performance of the new method is compared to that of rule-based methods.     

Competing designs for phase I clinical trials: a review

Phase I clinical trials are typically small, uncontrolled studies designed to determine a maximum tolerated dose of a drug which will be used in further testing. Two divergent schools have developed in designing phase I clinical trials. The first defines the maximum tolerated dose as a statistic computed from data, and hence it is identified, rather than estimated. The second defines the maximum tolerated dose as a parameter of a monotonic dose-response curve, and hence is estimated. We review techniques from both philosophies. The goal is to present these methods in a single package, to compare them from philosophical and statistical grounds, to hopefully clear up some common misconceptions, and to make a few recommendations. This paper is not a review of simulation studies of these designs, nor does it present any new simulations comparing these designs.     

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.     

Stopped hearts,amputated toes and NASA: contemporary legends among healthy volunteers in US phase I clinical trials

The first stage of testing new pharmaceuticals in humans is referred to as a phase I clinical trial. The purpose of these studies is to test the safety of the drugs and to establish appropriate doses that can later be given to patients. Most of these studies are conducted under controlled, in‐patient conditions using healthy volunteers who are paid for their participation. To explore healthy volunteers’ experiences in clinical trials, an ethnographic study was conducted at six in‐patient phase I clinics in the USA. In addition to the observation of clinic activities (from informed consent procedures to dosing to blood draws), 268 semi‐structured interviews were conducted, 33 with clinic staff and 235 with healthy volunteers. Drawing on this dataset, this article explores healthy volunteers’ exchange of contemporary legends about phase I clinical trials. In addition to potentially scaring the listener and communicating distrust in the medical community, these incredible stories help participants cope with perceived stigma and establish a gradient of risk of trial participation, creating potential boundaries to their participation in medical research. The article argues that contemporary legends play a productive role in society, shaping how people view themselves and others and influencing their decisions about risky activities.     

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 sequential analysis with survival endpoint in phase II clinical trials

Chen and Chaloner (Statist. Med. 2006; 25 :2956–2966. DOI: 10.1002/sim.2429 ) present a Bayesian stopping rule for a single‐arm clinical trial with a binary endpoint. In some cases, earlier stopping may be possible by basing the stopping rule on the time to a binary event. We investigate the feasibility of computing exact, Bayesian, decision‐theoretic time‐to‐event stopping rules for a single‐arm group sequential non‐inferiority trial relative to an objective performance criterion. For a conjugate prior distribution, exponential failure time distribution, and linear and threshold loss structures, we obtain the optimal Bayes stopping rule by backward induction. We compute frequentist operating characteristics of including Type I error, statistical power, and expected run length. We also briefly address design issues. Copyright © 2009 John Wiley & Sons, Ltd.     

A Bayesian time-to-event pharmacokinetic model for phase I dose-escalation trials with multiple schedules

Phase I dose-escalation trials must be guided by a safety model in order to avoid exposing patients to unacceptably high risk of toxicities. Traditionally, these trials are based on one type of schedule. In more recent practice, however, there is often a need to consider more than one schedule, which means that in addition to the dose itself, the schedule needs to be varied in the trial. Hence, the aim is finding an acceptable dose-schedule combination. However, most established methods for dose-escalation trials are designed to escalate the dose only and ad hoc choices must be made to adapt these to the more complicated setting of finding an acceptable dose-schedule combination. In this article, we introduce a Bayesian time-to-event model which takes explicitly the dose amount and schedule into account through the use of pharmacokinetic principles. The model uses a time-varying exposure measure to account for the risk of a dose-limiting toxicity over time. The dose-schedule decisions are informed by an escalation with overdose control criterion. The model is formulated using interpretable parameters which facilitates the specification of priors. In a simulation study, we compared the proposed method with an existing method. The simulation study demonstrates that the proposed method yields similar or better results compared with an existing method in terms of recommending acceptable dose-schedule combinations, yet reduces the number of patients enrolled in most of scenarios. The R and Stan code to implement the proposed method is publicly available from Github ( https://github.com/gunhanb/TITEPK_code ).     

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.     

Information‐adaptive clinical trials with selective recruitment and binary outcomes

Selective recruitment designs preferentially recruit individuals who are estimated to be statistically informative onto a clinical trial. Individuals who are expected to contribute less information have a lower probability of recruitment. Furthermore, in an information‐adaptive design, recruits are allocated to treatment arms in a manner that maximises information gain. The informativeness of an individual depends on their covariate (or biomarker) values, and how information is defined is a critical element of information‐adaptive designs. In this paper, we define and evaluate four different methods for quantifying statistical information. Using both experimental data and numerical simulations, we show that selective recruitment designs can offer a substantial increase in statistical power compared with randomised designs. In trials without selective recruitment, we find that allocating individuals to treatment arms according to information‐adaptive protocols also leads to an increase in statistical power. Consequently, selective recruitment designs can potentially achieve successful trials using fewer recruits thereby offering economic and ethical advantages. Copyright © 2017 John Wiley & Sons, Ltd.     

Two‐sample binary phase 2 trials with low type I error and low sample size

We address design of two‐stage clinical trials comparing experimental and control patients. Our end point is success or failure, however measured, with null hypothesis that the chance of success in both arms is p ₀ and alternative that it is p ₀ among controls and p ₁ > p ₀ among experimental patients. Standard rules will have the null hypothesis rejected when the number of successes in the (E)xperimental arm, E , sufficiently exceeds C , that among (C)ontrols. Here, we combine one‐sample rejection decision rules, , with two‐sample rules of the form E  ? C  > r to achieve two‐sample tests with low sample number and low type I error. We find designs with sample numbers not far from the minimum possible using standard two‐sample rules, but with type I error of 5% rather than 15% or 20% associated with them, and of equal power. This level of type I error is achieved locally, near the stated null, and increases to 15% or 20% when the null is significantly higher than specified. We increase the attractiveness of these designs to patients by using 2:1 randomization. Examples of the application of this new design covering both high and low success rates under the null hypothesis are provided. Copyright © 2017 John Wiley & Sons, Ltd.     

Combined criteria for dose optimisation in early phase clinical trials

This paper aims to investigate whether any bridge is possible between so-called best intention and D-optimum designs. It introduces combined criteria for dose optimisation in seamless phase I/II adaptive clinical trials. Each of the optimality criteria considers efficacy and toxicity as endpoints and is based on the probability of a successful outcome and on the determinant of the Fisher information matrix for estimation of the dose-response parameters. In addition, one of the criteria incorporates penalties for choosing a toxic or inefficacious dose. Starting with the lowest dose, the adaptive design selects the dose for each subsequent cohort that maximises the respective defined criterion. The methodology is illustrated with a dose-response model that assumes trinomial responses. Simulation studies show that the method is capable of identifying the optimal dose accurately without exposing many patients to toxic doses.     

Dose finding with drug combinations in cancer phase I clinical trials using conditional escalation with overdose control

We present a Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials. The goal is to estimate the maximum tolerated dose (MTD) as a curve in the two‐dimensional Cartesian plane. We use a logistic model to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity. The model is re‐parameterized in terms of parameters clinicians can easily interpret. Trial design proceeds using univariate escalation with overdose control, where at each stage of the trial, we seek a dose of one agent using the current posterior distribution of the MTD of this agent given the current dose of the other agent. At the end of the trial, an estimate of the MTD curve is proposed as a function of Bayes estimates of the model parameters. We evaluate design operating characteristics in terms of safety of the trial design and percent of dose recommendation at dose combination neighborhoods around the true MTD curve. We also examine the performance of the approach under model misspecifications for the true dose–toxicity relationship. Copyright © 2014 John Wiley & Sons, Ltd.     

An extension of the single threshold design for monitoring efficacy and safety in phase II clinical trials

Tan and Machin (biStat. Med. 2002; 21:1991-2012) introduce a Bayesian two-stage design for phase II clinical trials where the binary endpoint of interest is treatment efficacy. In this paper we propose an extension of their design to incorporate safety considerations. At each stage we define the treatment successful and deserving of further study if the total number of adverse events is sufficiently small and the number of responders who simultaneously do not experience any toxicity is sufficiently large. Therefore, our criterion is based on the joint posterior probability that the true overall toxicity rate and the true efficacy-and-safety rate are, respectively, smaller and larger than conveniently pre-specified target values. The optimal two-stage sample sizes are determined specifying a minimum threshold for the above-mentioned posterior probability, computed under the assumption that favorable outcomes have occurred. Besides describing the proposed design, we suggest how to construct informative prior scenarios and we also apply the reference algorithm to derive a non-informative prior distribution. Finally, some numerical results are provided and a real data application is illustrated.     

Adaptive group sequential design for phase II clinical trials: A Bayesian decision theoretic approach

Bayesian decision theoretic approaches (BDTAs) have been widely studied in the literature as tools for designing and conducting phase II clinical trials. However, full Bayesian approaches that consider multiple endpoints are lacking. Since the monitoring of toxicity is a major goal of phase II trials, we propose an adaptive group sequential design using a BDTA, which characterizes efficacy and toxicity as correlated bivariate binary endpoints. We allow trade‐off between the two endpoints. Interim evaluations are conducted group sequentially, but the number of interim looks and the size of each group are chosen adaptively based on current observations. We utilize a loss function consisting of two components: the loss associated with accruing, treating, and monitoring patients, and the loss associated with making incorrect decisions. The performance of our Bayesian modeling, and the operating characteristics of decision rules under a wide range of loss function parameters are evaluated using seven scenarios in a simulation study. Our method is illustrated in the context of a single‐arm phase II trial of bevacizumab, gemcitabine, and oxaliplatin in patients with metastatic pancreatic adenocarcinoma. Copyright © 2009 John Wiley & Sons, Ltd.     

Practical modifications to the time-to-event continual reassessment method for phase I cancer trials with fast patient accrual and late-onset toxicities

The goal of phase I cancer trials is to determine the highest dose of a treatment regimen with an acceptable toxicity rate. Traditional designs for phase I trials, such as the Continual Reassessment Method (CRM) and the 3 + 3 design, require each patient or a cohort of patients to be fully evaluated for the dose-limiting toxicity (DLT) before new patients can be enrolled. As such, the trial duration may be prohibitively long. The Time-to-Event Continual Reassessment Method (TITE-CRM, Cheung and Chappell, 2000) circumvents this limitation by allowing staggered patient accrual without the need for complete DLT follow-up of previously treated patients. However, in the setting of fast patient accrual and late-onset toxicities, the TITE-CRM results in overly aggressive dose escalation and exposes a considerable number of patients to toxic doses. We examine a modification to the TITE-CRM proposed by the original TITE-CRM creator and propose an alternative approach useful in this setting by incorporating an accrual suspension rule. A simulation study designed based on a neuro-oncology trial indicates that the modified methods provide a much improved degree of safety than the TITE-CRM while maintaining desirable design accuracy. The practical aspects of the proposed designs are discussed. The modifications presented are useful when planning phase I trials involving chemoradiation therapy.