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The continual reassessment method is a model-based procedure, described in the literature, used to determine the maximum tolerated dose in phase I clinical trials. The maximum tolerated dose can also be found under the framework of D-optimum design, where information is gathered in such a way so that asymptotic variability in the parameter estimates in minimised. This paper investigates the two methods under some realistic settings to explore any potential differences between them. Simulation studies for six plausible dose-response scenarios show that D-optimum design can work well in comparison with the continual reassessment method in many cases. The D-optimum design is also found to allocate doses from the extremes of the design region to the patients in a trial.

The continual reassessment method (CRM) is an adaptive design for Phase I trials whose operating characteristics, including appropriate sample size, probability of correctly identifying the maximum tolerated dose, and the expected proportion of participants assigned to each dose, can only be determined via simulation. The actual time to determine a final "best" design can take several hours or days, depending on the number of scenarios that are examined. The computational cost increases as the kernel of the one-parameter CRM design is expanded to other settings, including additional parameters, monitoring of both toxicity and efficacy, and studies of combinations of two agents. For a given vector of true DLT probabilities, we have developed an approach that replaces a simulation study of thousands of hypothetical trials with a single simulation. Our approach, which is founded on the consistency of the CRM, very accurately reflects the results produced by the simulation study, but does so in a fraction of time required by the simulation study. Relative to traditional simulations, we extensively examine how our method is able to assess the operating characteristics of a CRM design for a hypothetical trial whose characteristics are based upon a previously published Phase I trial. We also provide a metric of nonconsistency and demonstrate that although nonconsistency can impact the operating characteristics of our method, the degree of over- or under-estimation is unpredictable. As a solution, we provide an algorithm for maintaining the consistency of a chosen CRM design so that our method is applicable for any trial.

Background: This article studies the notion of irrational dose assignment in Phase I clinical trials. This property was recently defined by Zhou and colleagues as a dose assignment that fails to de-escalate the dose when two out of three, three out of six, or four out of six patients have experienced a dose-limiting toxicity event at the current dose level. The authors claimed that a drawback of the well-known continual reassessment method is that it can result in irrational dose assignments. The aim of this article is to examine this definition of irrationality more closely within the conduct of the continual reassessment method. Methods: Over a broad range of assumed dose-limiting toxicity probability scenarios for six study dose levels and a variety of target dose-limiting toxicity rates, we simulated 2000 trials of n = 36 patients. For each scenario, we counted the number of irrational dose assignments that were made by the continual reassessment method, according to the definitions of Zhou and colleagues. For each of the irrational decisions made, we classified the dose assignment as an underdose assignment, a target dose assignment, or an overdose assignment based on the true dose-limiting toxicity probability at that dose. Results: Across eight dose-toxicity scenarios, there were a total of 181,581 dose assignments made in the simulation study. Of these assignments, 8165 (4.5%) decisions were made when two out of three, three out of six, or four out of six patients had experienced a dose-limiting toxicity at the current dose. Of these 8165 decisions, 1505 (18.4%) recommended staying at the current dose level and would therefore be classified as irrational by Zhou and colleagues. Among the irrational decisions, 41.2% were misclassified, meaning they were made either at the true target dose (17.9%) or at a true underdose (23.3%). The remaining 58.8% were made at a true overdose and therefore truly irrational. Overall, irrational dose assignments comprised Conclusion: Zhou and colleagues argue that the behavior of the continual reassessment method is disturbing due to its ability to make irrational dose assignments. These definitions are based on rules that mimic the popular 3 + 3 design, which should not be the benchmark used to construct guidelines for trial conduct of modern Phase I methods. Our study illustrates that these dose assignments occur very seldom in the continual reassessment method and that even when they do occur, they can often be considered sensible when accounting for all accumulated data in the study.

This article considers the problem of designing Phase I-II clinical trials with delayed toxicity and efficacy outcomes. The proposed design is motivated by a Phase I-II study evaluating all-trans retinoic acid (ATRA) in combination with a fixed dose of daratumumab in the treatment of relapsed or refractory multiple myeloma. The primary objective of the study is to identify a dose that maximizes efficacy and has an acceptable level of toxicity. The toxicity endpoint is observed in one cycle of therapy (i.e., 4 weeks) while the efficacy endpoint is assessed after 8 weeks of treatment. The difference in endpoint observation windows causes logistical challenges in conducting the trial, since it is not practical to wait until both outcomes for each patient have been fully observed before sequentially assigning the dose of a newly eligible patient. In order to avoid delays in treatment for newly enrolled patients and to accelerate trial progress, we generalize the time-to-event continual reassessment method (TITE-CRM) to bivariate outcomes. Simulation studies are conducted to evaluate the proposed method, and we found that the proposed design is able to accurately select doses that maximize efficacy and have acceptable toxicity, while using all available information in allocating patients at the time of dose assignment. We compare the proposed methodology to two existing methods in the area.

Early phase trials provide crucial information about new medicines that allow them to be taken forward into larger confirmatory studies. Paediatric early phase studies are becoming more common, particularly in the era of precision therapy. There are almost 600 active paediatric phase I/II trials listed on clinicaltrials.gov. Conventionally, early phase dose-escalation trials use rule-based designs such as the 3+3 to guide dose decisions. A trial is considered to have a rule-based design if predefined rules are used to guide decisions to escalate, continue or de-escalate based on the observed toxicities at the current dose. Though it is well established that model-based design is generally superior to rule-based design, its uptake remains low. Trials with a model-based design use statistical models to guide decisions on which dose levels to give the next patient(s), based on the targeted toxicity level and previous information. In this article, we review one of the most commonly used model-based designs, the continual reassessment method. The goal of any phase I study is to find a recommended dose of a new therapy to advance in subsequent studies. This can be defined in different ways; one of the most common being the maximum tolerated dose (MTD). One key question is: ‘What level of toxicity is acceptable based on the expected benefits of treatment?’ The MTD is defined as the dose expected to cause a degree of medically undesirable dose-limiting toxicity (DLT) in an acceptable specified proportion of participants . The latter is referred to as the target toxicity level (TTL). This level will differ depending on the expected benefits of the treatment as well as the severity of the expected toxicity for the specific intervention. This relationship is illustrated in figure 1. Figure 1 An illustration of the relationship between dose …

Drug combination trials are increasingly common nowadays in clinical research. However, very few methods have been developed to consider toxicity attributions in the dose escalation process. We are motivated by a trial in which the clinician is able to identify certain toxicities that can be attributed to one of the agents. We present a Bayesian adaptive design in which toxicity attributions are modeled via Copula regression and the maximum tolerated dose (MTD) curve is estimated as a function of model parameters. The dose escalation algorithm uses cohorts of two patients, following the continual reassessment method (CRM) scheme, where at each stage of the trial, we search for the dose of one agent given the current dose of the other agent. The performance of the design is studied by evaluating its operating characteristics when the underlying model is either correctly specified or misspecified. We show that this method can be extended to accommodate discrete dose combinations.

Phase I trial designs in oncology aim to determine the maximum tolerated doses for agents of interest. Traditionally, dose-finding designs for phase I trials can be classified as rule-/algorithm- or model-based designs. In Chap. 2, we focused on rule-based designs; in the present chapter, we focus on model-based designs. Model-based designs assume a certain statistical model for the monotonic dose–toxicity relationship to borrow strength across doses, and originate from the continual reassessment method (CRM). The present chapter provides a detailed description of the concepts, theories, properties, and advantages and disadvantages of the CRM, and also overview designs that are related to the CRM or that constitute extended versions of this method.

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.

Summary In determining dose limiting toxicities in phase I studies, it is necessary to attribute adverse events to being drug related or not. Such determination is subjective and may introduce bias. We develop methods for removing or at least diminishing the effect of this bias on the estimation of the maximum tolerated dose. The approach that we suggest takes into account the subjectivity in the attribution of adverse events by using model-based dose escalation designs. The results show that gains can be achieved in terms of accuracy by recovering information lost to biases. These biases are a result of ignoring the errors in toxicity attribution.

The design of phase I studies is often challenging, because of limited evidence to inform study protocols. Adaptive designs are now well established in cancer but much less so in other clinical areas. A phase I study to assess the safety, pharmacokinetic profile and antiretroviral efficacy of C34‐PEG4‐Chol, a novel peptide fusion inhibitor for the treatment of HIV infection, has been set up with Medical Research Council funding. During the study workup, Bayesian adaptive designs based on the continual reassessment method were compared with a more standard rule‐based design, with the aim of choosing a design that would maximise the scientific information gained from the study. The process of specifying and evaluating the design options was time consuming and required the active involvement of all members of the trial's protocol development team. However, the effort was worthwhile as the originally proposed rule‐based design has been replaced by a more efficient Bayesian adaptive design. While the outcome to be modelled, design details and evaluation criteria are trial specific, the principles behind their selection are general. This case study illustrates the steps required to establish a design in a novel context. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd

Multiple Bayesian adaptive designs have been proposed for Phase I clinical trials since the continual reassessment method (CRM) was proposed by O’Quigley et al. (1990). Focused on dose-finding in cancer studies, the CRM seeks to allocate new patients to an estimated maximum tolerable dose (MTD). Later, Whitehead and Brunier (1995) applied Bayesian decision theory to maximize statistical information for the MTD when allocating new patients. The two allocation rules reflect conflicting perspectives. The CRM emphasizes individual-level ethics, whereas the method of Whitehead and Brunier (1995) emphasizes population-level ethics. In the design of a Phase I clinical trial to investigate hyperthermic intraperitoneal chemotherapy (HIPEC) we sought to compromise the two perspectives. To this end, we propose a novel dose allocation design referred to as the balanced information gain method. We first decompose the loss function used by Whitehead and Brunier and then modify it with a tuning parameter that al...

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.

A widely used approach in designing the phase I clinical trial is continual reassessment method (CRM). In this paper, we prove that under simple power model and logistic model, the way CRM selects the next dose level is highly efficient from the perspective of optimal design. More specifically, for simple power model, we show that the optimal design selects the dose level such that the corresponding toxicity rate is around 0.2; as for logistic model, we show that CRM is indeed optimal, which will justify the efficiency of the algorithm in theory.

The Bayesian model averaging continual reassessment method (CRM) is a Bayesian dose-finding design. It improves the robustness and overall performance of the continual reassessment method (CRM) by specifying multiple skeletons (or models) and then using Bayesian model averaging to automatically favor the best-fitting model for better decision making. Specifying multiple skeletons, however, can be challenging for practitioners. In this paper, we propose a default way to specify skeletons for the Bayesian model averaging CRM. We show that skeletons that appear rather different may actually lead to equivalent models. Motivated by this, we define a nonequivalence measure to index the difference among skeletons. Using this measure, we extend the model calibration method of Lee and Cheung (2009) to choose the optimal skeletons that maximize the average percentage of correct selection of the maximum tolerated dose and ensure sufficient nonequivalence among the skeletons. Our simulation study shows that the proposed method has desirable operating characteristics. We provide software to implement the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

The majority of phase I methods for multi-agent trials have focused on identifying a single maximum tolerated dose combination (MTDC) among those being investigated. Some published methods in the area have been based on the notion that there is no unique MTDC and that the set of dose combinations with acceptable toxicity forms an equivalence contour in two dimensions. Therefore, it may be of interest to find multiple MTDCs for further testing for efficacy in a phase II setting. In this paper, we present a new dose-finding method that extends the continual reassessment method to account for the location of multiple MTDCs. Operating characteristics are demonstrated through simulation studies and are compared with existing methodology. Some brief discussion of implementation and available software is also provided. Copyright © 2016 John Wiley & Sons, Ltd.

Background Theoretically, the ideal volume of local anaesthetic for adductor canal block (ACB) would ensure sufficient filling of the canal and avoid proximal spread to the femoral triangle. In this dose-finding study, we aimed to investigate the minimal effective volume for an ACB needed to fill the adductor canal distally in at least 95% of patients (ED95). Methods We performed a blinded trial, enrolling 40 healthy men. All subjects received an ACB with lidocaine 1%. Volumes were assigned sequentially to the subjects using the continual reassessment method followed by Bayesian analysis to determine the ED95. Distal filling of the adductor canal was assessed by magnetic resonance imaging (primary outcome). Secondary outcomes were the effect of volume on proximal spread to the femoral triangle (also assessed by magnetic resonance imaging), quadriceps muscle weakness (decrease by ≥25% from baseline) and sensory block. Results The ED95 was 20 ml, with an estimated probability of sufficiently filling the canal of 95.1% (95% credibility interval: 0.91–0.98). Proximal spread to the femoral triangle was seen in 0/4 (0%), 7/12 (58%), 4/8 (50%), and 8/16 (50%) subjects with the 5, 10, 15, and 20 ml doses, respectively (P=0.25). Seven subjects had a reduction in muscle strength, but there was no difference between groups (P=0.85). Conclusions For an ACB, the dose closest to the ED95 needed to fill the adductor canal distally was 20 ml. There was no significant correlation between volume and proximal spread or muscle strength. Clinical trial registration NCT02033356.

In this article, we propose and evaluate three alternative randomization strategies to the adaptive randomization (AR) stage used in a seamless Phase I/II dose-finding design. The original design was proposed by Wages and Tait in 2015 for trials of molecularly targeted agents in cancer treatments, where dose-efficacy assumptions are not always monotonically increasing. Our goal is to improve the design's overall performance regarding the estimation of optimal dose as well as patient allocation to effective treatments. The proposed methods calculate randomization probabilities based on the likelihood of every candidate model as opposed to the original design which selects the best model and then randomizes doses based on estimations from the selected model. Unlike the original method, our proposed adaption does not require an arbitrarily specified sample size for the adaptive randomization stage. Simulations are used to compare the proposed strategies and a final strategy is recommended. Under most scenarios, our recommended method allocates more patients to the optimal dose while improving accuracy in selecting the final optimal dose without increasing the overall risk of toxicity.

Background Limited options are available for dose-finding clinical trials requiring group-specific dose selection. While conducting parallel trials for groups is an accessible approach to group-specific dose selection, this approach allows for maximum tolerated dose selection that does not align with clinically meaningful group order information. Methods The two-stage continual reassessment method is developed for dose-finding in studies involving three or more groups where group frailty order is known between some but not all groups, creating a partial order. This is an extension of the existing continual reassessment method shift model for two ordered groups. This method allows for dose selection by group, where maximum tolerated dose selection follows the known frailty order among groups. For example, if a group is known to be the most frail, the recommended maximum tolerated dose for this group should not exceed the maximum tolerated dose recommended for any other group. Results With limited alternatives for dose-finding in partially ordered groups, this method is compared to two alternatives: (1) an existing method for dose-finding in partially ordered groups which is less computationally accessible and (2) independent trials for each group using the two-stage continual reassessment method. Simulation studies show that when ignoring information on group frailty, using independent continual reassessment method trials by group, 30% of simulations would result in maximum tolerated dose selection that is out of order between groups. In addition, the two-stage continual reassessment method for partially ordered groups selects the maximum tolerated dose more often and assigns more patients to the maximum tolerated dose compared to using independent continual reassessment method trials within each group. Simulation results for the proposed method and the less computationally accessible approach are similar. Conclusion The proposed continual reassessment method for partially ordered groups ensures appropriate maximum tolerated dose order and improves accuracy of maximum tolerated dose selection, while allowing for trial implementation that is computationally accessible.

An extension of the isotonic regression based phase I clinical trial design is presented that incorporates partial follow-up times into estimation of the raw toxicity probabilities. This phase I clinical trial design, called the TITE-IR design, drastically decreases average trial duration by allowing patients to be treated immediately after being enrolled in a phase I clinical trial. The TITE-IR design does not require specification of a prior skeleton of toxicity probabilities like the continual reassessment method, has an additional trial parameter for controlling aggressiveness of dose escalation, and has an easily understood formula for estimating toxicity probabilities. An R statistical software package is described in detail in the appendix for simulating and implementing the design. A simulation study shows that the TITE-IR design outperforms the 3 + 3 design in terms of selecting the true maximum tolerated dose and results in shorter trial times, without a large loss in efficiency, compared to the isotonic regression design and Storer's up-and-down design D. These properties make the TITE-IR design a more appealing option to clinicians than the two most commonly used 3 + 3 designs and the isotonic regression design with larger follow-up windows for toxicity. Keywords: Phase I, Clinical trials, Dose-finding, Time-to-event, Isotonic regression

Following phase I dose-finding oncology trials completed in Western countries, Asian investigators often conduct further phase I trials to determine the maximum tolerated dose for Asian patients. This may be due to concerns about possible differences in treatment tolerability between Caucasian and Asian patient groups. Our proposed approach aims to appropriately borrow strength from a previous Caucasian trial to improve the maximum tolerated dose determination in an Asian population of patients. We design an Asian phase I trial using the Bayesian continual reassessment method. First we analyze toxicity data from a Caucasian trial to derive the prior distributions for a subsequent Asian trial. Then, we calibrate the informativeness of the prior distributions according to prior effective sample size defined by Morita et al. Extensive simulation studies demonstrate favourable operating characteristics of the proposed method, compared with two methods based on power and noninformative priors, respectively.

This paper proposes a novel criterion for the allocation of patients in Phase~I dose-escalation clinical trials aiming to find the maximum tolerated dose (MTD). Conventionally, using a model-based approach the next patient is allocated to the dose with the toxicity estimate closest (in terms of the absolute or squared distance) to the maximum acceptable toxicity. This approach, however, ignores the uncertainty in point estimates and ethical concerns of assigning a lot of patients to overly toxic doses. Motivated by recent discussions in the theory of estimation in restricted parameter spaces, we propose a criterion which accounts for both of these issues. The criterion requires a specification of one additional parameter only which has a simple and intuitive interpretation. We incorporate the proposed criterion into the one-parameter Bayesian continual reassessment method (CRM) and show, using simulations, that it results in the same proportion of correct selections on average as the original design, but in fewer mean number of toxic responses. A comparison to other model-based dose-escalation designs demonstrates that the proposed design can result in either the same mean accuracy as alternatives but fewer number of toxic responses, or in a higher mean accuracy but the same number of toxic responses. We conclude that the new criterion makes the existing model-based designs more ethical without losing efficiency in the context of Phase I clinical trials., 26 pages, 7 figures, 5 tables

Background Broad implementation of model-based dose-finding methods, such as the continual reassessment method (CRM), has been limited, with traditional or modified 3 + 3 designs remaining in frequent use. Part of the reason is the lack of reliable, easy-to-use, and robust software tools for designing and implementing more efficient designs. Results With the aim of augmenting broader implementation of model-guided methods, we have developed a web application for the Bayesian CRM in the R programming language using the Shiny package. The application has two components, simulation and implementation. Within the application, one has the ability to generate simulated operating characteristics for the study design phase, and to sequentially provide the next dose recommendation for each new accrual or cohort based on the current data for the study implementation phase. At the conclusion of the study, it can be used to estimate the maximum tolerated dose (MTD). The web tool requires no programming knowledge, and it is free to access on any device with an internet browser. Conclusions The application provides the type of simulation information that aid clinicians and reviewers in understanding operating characteristics for the accuracy and safety of the CRM, which we hope will augment phase I trial design. We believe that the development of this software will facilitate more efficient collaborations within study teams conducting single-agent dose-finding trials.

The chapter provides comprehensive review and comparison on single-agent and drug-combination phase I clinical trial designs, with particular focus on novel designs, including the continual reassessment method (CRM) , the Bayesian model averaging CRM (BMA-CRM) , the modified toxicity probability interval (mTPI) design, the Bayesian optimal interval (BOIN) design, and the Keyboard design. We discuss the pros and cons of these designs. Numerical study shows that the CRM, BOIN and Keyboard designs provide comparable, excellent operating characteristics, and each outperforms the mTPI design. These designs are more likely to correctly select the MTD and less likely to overdose a large percentage of patients. The extensions of the BOIN design to drug combination trials are briefly discussed. The software to implement these innovative designs is described and provided.

Traditionally, phase I trials are designed to determine the MTD of a new agent based solely on toxicity, regardless of the efficacy. The determination of an optimal dose based on the joint assessment of toxicity and efficacy of the drug in phase I dose-finding trials may be reasonable in some cases. The various types of incorporation of toxicity and efficacy outcomes into dose-finding methods have been developed. Among them, in this chapter, we overview four methods: (i) the bivariate continual reassessment method, (ii) Bayesian method based on the efficacy–toxicity trade-off, (iii) Bayesian method for evaluating binary toxicity and continuous efficacy outcomes, and (iv) the method based on the Bayesian Model Averaging (BMA).

Phase I trials evaluating the safety of multi-drug combinations are becoming more common in oncology. Despite the emergence of novel methodology in the area, it is rare that innovative approaches are used in practice. In this article, we review three methods for Phase I combination studies that are easy to understand and straightforward to implement. We demonstrate the operating characteristics of the designs through illustration in a single trial, as well as through extensive simulation studies, with the aim of increasing the use of novel approaches in phase I combination studies. Design specifications and software capabilities are also discussed.

Conventional dose finding methods require the dichotomization of toxicity outcome measures generally collected in an ordinal scale. To improve efficiency and include more information on the gradation of toxicities, a sequential likelihood procedure that accounts for multiple toxicity constraints is proposed to differentiate the tolerance for toxicity of various degrees of severity under a novel class of multiplicative models, and the asymptotic properties of the procedure under certain model misspecification are established.

Purpose: To estimate the maximum tolerated dose (MTD) and to determine the recommended dose (RD) of stereotactic body radiation therapy (SBRT) for peripheral T2N0M0 non-small cell carcinoma (NSCLC) with target volume (PTV) < 100 cc. Materials and methods: The continual reassessment method (CRM) was used to determine the dose level that patients should be assigned to and to estimate the MTD. Dose limiting toxicity (DLT) was grade 3 radiation pneumonitis (RP) within 180 days after the start of SBRT, grade 2 RP was used as a surrogate DLT. The RD was equal to the MTD. The dose was prescribed at D95 of the PTV. Results: Fifteen patients were accrued. Only 1 experienced grade 2 RP at 60 Gy in 4 fractions. It was difficult to fulfill the dose constraints at 60 Gy in 4 fractions, and the maximum dose level assigned by CRM was changed to 55 Gy in 4 fractions. The lower limit of 95% of the credible interval exceeded the adjacent level, and the RD was determined as 55 Gy in 4 fractions. Conclusions: The RD of SBRT for peripheral T2N0M0 NSCLC with PTV < 100 cc was determined to be 55 Gy in 4 fractions.

Summary In phase I trials, effectively treating patients and minimizing the chance of exposing them to subtherapeutic and overly toxic doses are clinicians' top priority. Motived by this practical consideration, we propose Bayesian optimal interval (BOIN) designs to find the maximum tolerated dose and to minimize the probability of inappropriate dose assignments for patients. We show, both theoretically and numerically, that the BOIN design not only has superior finite and large sample properties but also can be easily implemented in a simple way similar to the traditional ‘3+3’ design. Compared with the well-known continual reassessment method, the BOIN design yields comparable average performance to select the maximum tolerated dose but has a substantially lower risk of assigning patients to subtherapeutic and overly toxic doses. We apply the BOIN design to two cancer clinical trials.

Introduction The continual reassessment method (CRM) is a model-based design for phase I trials, which aims to find the maximum tolerated dose (MTD) of a new therapy. The CRM has been shown to be more accurate in targeting the MTD than traditional rule-based approaches such as the 3 + 3 design, which is used in most phase I trials. Furthermore, the CRM has been shown to assign more trial participants at or close to the MTD than the 3 + 3 design. However, the CRM’s uptake in clinical research has been incredibly slow, putting trial participants, drug development and patients at risk. Barriers to increasing the use of the CRM have been identified, most notably a lack of knowledge amongst clinicians and statisticians on how to apply new designs in practice. No recent tutorial, guidelines, or recommendations for clinicians on conducting dose-finding studies using the CRM are available. Furthermore, practical resources to support clinicians considering the CRM for their trials are scarce. Methods To help overcome these barriers, we present a structured framework for designing a dose-finding study using the CRM. We give recommendations for key design parameters and advise on conducting pre-trial simulation work to tailor the design to a specific trial. We provide practical tools to support clinicians and statisticians, including software recommendations, and template text and tables that can be edited and inserted into a trial protocol. We also give guidance on how to conduct and report dose-finding studies using the CRM. Results An initial set of design recommendations are provided to kick-start the design process. To complement these and the additional resources, we describe two published dose-finding trials that used the CRM. We discuss their designs, how they were conducted and analysed, and compare them to what would have happened under a 3 + 3 design. Conclusions The framework and resources we provide are aimed at clinicians and statisticians new to the CRM design. Provision of key resources in this contemporary guidance paper will hopefully improve the uptake of the CRM in phase I dose-finding trials. Electronic supplementary material The online version of this article (10.1186/s12874-018-0638-z) contains supplementary material, which is available to authorized users.

Background The purpose of this study was to determine the maximum tolerated dose (MTD, the dose of causing 10% respiratory depression) of intranasal sufentanil (SUF) and midazolam (MID) for sedation during gastroscopy by continual reassessment method (CRM). Methods Patients (18-65 years old) scheduled for gastroscopy were recruited in this study. Subjects received intranasal SUF and MID for sedation. The dose of MID (5 mg) was fixed, while the dose of SUF was increased progressively (six incremental doses ranging from 0-0.60 μg/kg, n = 3 for each dose). The first cohort received a conservative, predetermined dose of 5 mg MID and 0 μg/kg SUF, subsequent cohorts received doses of SUF that were determined by the responses of all previous patients using Bayesian-based software. The dose allocated to the next cohort is the one with an updated posterior response probability closest to 10%. Results Thirty Chinese patients scheduled for gastroscopy were included. Probability of respiratory depression at each dose was as follows: 5 mg MID + 0 μg/kg SUF, 0.4%; 5 mg MID + 0.1 μg/kg SUF, 0.8%; 5 mg MID + 0.2 μg/kg SUF, 1.8%; 5 mg MID + 0.3 μg/kg SUF, 3.7%; 5 mg MID + 0.4 μg/kg SUF, 9.9%; 5 mg MID + 0.5 μg/kg SUF, 17.8%; 5 mg MID + 0.6 μg/kg SUF, 36.0%. Conclusion The MTD of intranasal MID and SUF for sedation during gastroscopy causing 10% respiratory depression is 5 mg MID + 0.4 μg/kg SUF, based on CRM.

Background Dose-finding trials are essential to drug development as they establish recommended doses for later-phase testing. We aim to motivate wider use of model-based designs for dose-finding, such as the continual reassessment method (CRM). Methods We carried out a literature review of dose-finding designs and conducted a survey to identify perceived barriers to their implementation. Results We describe the benefits of model-based designs (flexibility, superior operating characteristics, extended scope), their current uptake, and existing resources. The most prominent barriers to implementation of a model-based design were lack of suitable training, chief investigators’ preference for algorithm-based designs (e.g., 3+3), and limited resources for study design before funding. We use a real-world example to illustrate how these barriers can be overcome. ConclusionThere is overwhelming evidence for the benefits of CRM. Many leading pharmaceutical companies routinely implement model-based designs. Our analysis identified barriers for academic statisticians and clinical academics in mirroring the progress industry has made in trial design. Unified support from funders, regulators, and journal editors could result in more accurate doses for later-phase testing, and increase the efficiency and success of clinical drug development. We give recommendations for increasing the uptake of model-based designs for does-finding trials in academia.

Background A comparison of the effective dose in 50% of patients (ED50) has suggested that the potency of levobupivacaine lies between that of bupivacaine and ropivacaine. However, for clinical purposes, knowledge and use of doses close to the ED95 are more relevant. This study was designed to determine the EC80 (effective concentration) for both epidural levobupivacaine and ropivacaine using the Continual Reassessment Method (CRM) during obstetric analgesia. Methods In this double-blind randomised study, term parturients were included by cohorts of 6 if cervical dilatation was ≤ 5 cm and visual analogue pain score (VAPS) > 30 mm. Efficacy was defined by a decrease of VAPS to a value ≤ 10, thirty minutes after epidural injection of 20 mL of levobupivacaine or ropivacaine. The first cohort received the lowest dose. Every next cohort received a dose according to the response's probability calculated using a Bayesian method, incorporating data from all consecutive previous patients. In addition, a logistic equation was fitted a posteriori to the whole data set to determine the whole dose-probability curve. Results Fifty-four patients were enrolled. Levobupivacaine 0.17% and ropivacaine 0.2% gave probabilities of success of 82% and 72% respectively. By fitting the logistic model to the data, the concentration leading to a probability of 0.8 (EC80) was 0.14% for levobupivacaine and 0.24% for ropivacaine while the EC50 were 0.09% for levobupivacaine and 0.17% for ropivacaine, respectively. Conclusion This study suggests that epidural levobupivacaine used as the sole drug for labour analgesia has an EC80 lower than that of ropivacaine.

An efficient phase I trial is a crucial step in developing a new drug in a safe and timely manner. The main objective of a phase I trial is to determine the maximum tolerated dose in order to recommend the dose for a phase II trial. There are many designs that are implemented in phase I trials. Rule-based designs such as the traditional 3 + 3 method and rolling six design are easy to implement and assess for safety using a conservative approach. Model-based designs such as the continual reassessment method and the time-to-event continual reassessment method use mathematical models to increase the precision of dose estimation. The advantages and shortcomings of these designs, along with other designs, are reviewed.

The ever-increasing pace of development of novel therapies mandates efficient methodologies for assessment of their tolerability and activity. Evidence increasingly support the merits of model-based dose-finding designs in identifying the recommended phase II dose compared with conventional rule-based designs such as the 3 + 3 but despite this, their use remains limited. Here, we propose a useful tool, dose transition pathways (DTP), which helps overcome several commonly faced practical and methodologic challenges in the implementation of model-based designs. DTP projects in advance the doses recommended by a model-based design for subsequent patients (stay, escalate, de-escalate, or stop early), using all the accumulated information. After specifying a model with favorable statistical properties, we utilize the DTP to fine-tune the model to tailor it to the trial's specific requirements that reflect important clinical judgments. In particular, it can help to determine how stringent the stopping rules should be if the investigated therapy is too toxic. Its use to design and implement a modified continual reassessment method is illustrated in an acute myeloid leukemia trial. DTP removes the fears of model-based designs as unknown, complex systems and can serve as a handbook, guiding decision-making for each dose update. In the illustrated trial, the seamless, clear transition for each dose recommendation aided the investigators' understanding of the design and facilitated decision-making to enable finer calibration of a tailored model. We advocate the use of the DTP as an integral procedure in the co-development and successful implementation of practical model-based designs by statisticians and investigators. Clin Cancer Res; 23(24); 7440–7. ©2017 AACR.

We describe a dose escalation algorithm for drug combinations in cancer phase I clinical trials. Parametric models for describing the association between the doses and the probability of dose limiting toxicity are used assuming univariate monotonicity of the dose-toxicity relationship. Trial design proceeds using the continual reassessment method, where at each stage of the trial, we seek the dose of one agent with estimated probability of toxicity closest to a target probability of toxicity given the current dose of the other agent. A Bayes estimate of the maximum tolerated dose (MTD) curve is proposed at the conclusion of the trial for continuous doses or a set of MTDs is determined in the case of discrete dose levels. 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 under various model generated scenarios and misspecification. The method is further assessed for varying algorithms enrolling cohorts of two and three patients receiving different doses and compared to previous approaches such as escalation with overdose control and two-dimensional design.

The continual reassessment method (CRM) was proposed in a Bayesian framework whereby the first patient is assigned to the prior guess of the maximum tolerated dose which is usually not the lowest dose level. This assignment may lead to safety concerns in practice because physicians usually prefer not to skip lower dose levels before escalating to the higher dose levels. The two-stage CRM was proposed to address such concern whereby model based dose escalation is preceded by a pre-specified escalating sequence starting from the lowest dose level. While a theoretical framework to build the two-stage CRM has been proposed, the selection of the initial dose escalating sequence, generally referred to as the initial design, remains arbitrary, either by specifing cohorts of three patients or by trial and error through extensive simulations. Motivated by a currently ongoing oncology dose finding study for which physicians stated their desire to start from the lowest dose even though the maximum tolerated dose was thought to be one of the higher dose levels, we proposed a systematic approach for selecting the initial design for the two-stage CRM. The initial design obtained using the proposed algorithm yields better operating characteristics compared to using a cohort of three initial design with a calibrated CRM. The proposed algorithm simplifies and provides a systematic approach for the selection of initial design for the two-stage CRM. Moreover, initial designs to be used as reference for planning a two-stage CRM are provided.