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Background: Dose-finding studies in anaesthesiology aim to target the effective dose (ED) of an anaesthetic agent in a specific population. The common dose-finding designs used are the up and down method (UDM), the biased-coin up and down (BCD), and the continual reassessment method (CRM). Although the advantages of CRM over the UDM and BCD methods have been described in the statistical literature in terms of precision and direct estimation of ED, CRM may also offer attractive properties from an ethical point of view. Methods: Based on Monte Carlo simulations, this article aims to compare the three methods with regard to 1) their ability to find as close an estimate as possible for the ED95 or ED90 and 2) the total number of patients needed to treat and the number of failures. Results: In contrast to BCD and UDM, CRM does find an estimate for ED95 and ED90. UDM underestimates both ED95 and ED90. BCD is close to the targeted EDs when the starting dose does not exceed the ED of interest, otherwise it overestimates it. CRM with cohorts of two patients is closest to the ED of interest independently of the starting doses. CRM requires between 20 and 50 observations, UDM should include 90 patients, and BCD 100 or 60 observations. Lastly, CRM is associated with fewer failures, compared with BCD and UDM. Conclusions: Based on Monte Carlo simulations, our work suggests that the UDM is not an adequate dose-finding method because it underestimates the ED of interest. Compared with BCD, CRM offers the advantages of being more efficient, requires fewer patients to be included, and is associated with fewer failures.

Dose-finding clinical trials in oncology estimate the maximum tolerated dose (MTD), based on toxicity obtained from the clinician’s perspective. While the collection of patient-reported outcomes (PROs) has been advocated to better inform treatment tolerability, there is a lack of guidance and methods on how to use PROs for dose assignments and recommendations. The PRO continual reassessment method (PRO-CRM) has been proposed to formally incorporate PROs into dose-finding trials. In this paper, we propose two extensions of the PRO-CRM, which allow continuous enrollment of patients and longer toxicity observation windows to capture late-onset or cumulative toxicities by using a weighted likelihood to include the partial toxicity follow-up information. The TITE-PRO-CRM uses both the PRO and the clinician’s information during the trial for dose assignment decisions and at the end of the trial to estimate the MTD. The TITE-CRM + PRO uses clinician’s information solely to inform dose assignments during the trial and incorporates PRO at the end of the trial for the estimation of the MTD. Simulation studies show that the TITE-PRO-CRM performs similarly to the PRO-CRM in terms of dose recommendation and assignments during the trial while almost halving trial duration in case of an accrual of two patients per observation window. The TITE-CRM + PRO slightly underperforms compared to the TITE-PRO-CRM, but similar performance can be attained by requiring larger sample sizes. We also show that the performance of the proposed methods is robust to higher accrual rates, different toxicity hazards, and correlated time-to-clinician toxicity and time-to-patient toxicity data. [ABSTRACT FROM AUTHOR]

In many phase 1 oncology trials of immunotherapies, no dose‐limiting toxicities are observed and the maximum tolerated dose cannot be identified. In these settings, dose‐finding can be guided by a biomarker of response rather than the occurrences of dose‐limiting toxicity. The recommended phase 2 dose can be defined as the dose with mean response equal to a prespecified value of a continuous response biomarker. To target the mean of a continuous biomarker, we build on the idea of the continual reassessment method and the quasi‐Bernoulli likelihood. We extend the design to a problem of finding the recommended phase 2 dose combination in a trial with multiple immunotherapies. [ABSTRACT FROM AUTHOR]

This article considers the concept of designing Phase I clinical trials using both clinician- and patient-reported outcomes to adaptively allocate study participants to tolerable doses and determine the maximum tolerated dose (MTD) at the study conclusion. We describe an application of a Bayesian form of the patient-reported outcomes continual reassessment method (PRO-CRMB) in an ongoing Phase I study of adjuvant hypofractionated whole pelvis radiation therapy (WPRT) in endometrial cancer (NCT04458402). The study's primary objective is to determine the MTD per fraction of WPRT, defined by acceptable clinician- and patient-reported DLT rates. We conduct simulation studies of the operating characteristics of the design and compared them to a rule-based approach. We illustrate that the PRO-CRMB makes appropriate dose assignments during the study to give investigators and reviewers an idea of how the method behaves. In simulation studies, the PRO-CRMB demonstrates superior performance to a 5 + 2 stepwise design in terms of recommending target treatment courses and allocating patients to these courses. The design is accompanied by an easy-to-use R shiny web application to simulate operating characteristics at the design stage and sequentially update dose assignments throughout the trial's conduct. [ABSTRACT FROM AUTHOR]

We revisit our earlier extension of the CRM and introduce a new version of the dose escalation procedure. Its performance is assessed by simulation, with respect to a template of five operating measures BEARS (Benchmark, Efficiency, Accuracy, Reliability, Safety) that was used in Zhang, Wang and Yang. Our goal is to correctly identify the maximum tolerated dose with a high probability while protecting trial subjects from undue risk of toxicity. Simulation results show that this may be achieved for some considered scenarios, selective models, and considered competing designs. We discuss the pros and cons of the new version of the dose escalation procedure in the face of treatment versus experimentation dilemma. [ABSTRACT FROM AUTHOR]

For phase I trials, the subgroup‐specific time‐to‐event (Sub‐TITE) design identifies the maximum tolerated dose (MTD) separately in 2+ heterogeneous patient subgroups. Sub‐TITE allows borrowing strength and dynamic clustering across subgroups from the trial's start, but delaying the initiation of borrowing and clustering may improve trial accuracy. We propose the 2‐stage Sub‐TITE (2S‐Sub‐TITE) design in which the trial starts by estimating separate models per subgroup, and then initiates the Sub‐TITE design at some pre‐specified point of patient accrual. We evaluate the operating characteristics of the 2S‐Sub‐TITE design using simulations. Nine configurations of the 2S‐Sub‐TITE design (varying in timing of initiation of borrowing/clustering and prior probability of subgroup heterogeneity, p_hetero) and three control methods were compared across 1000 randomly‐generated true toxicity probability scenarios. Effects of priors, sample size, escalation rules, target toxicity probability, accrual rate, and number of subgroups were evaluated. Metrics included: proportion of correct selection (PCS) of the true MTD, and average number of toxicities incurred. Among the 5 2S‐Sub‐TITE configurations (out of 9 total) with the highest PCS (45%) when the subgroup heterogeneity assumption is correct (all of which out‐perform the control methods by 2%–6%), the configuration which enables borrowing and clustering allowance with p_hetero = 0.7 starting at 75% patient accrual best minimizes toxicities as well as losses in accuracy if the heterogeneity assumption is incorrect. For trials with high confidence in subgroup heterogeneity, the 2S‐Sub‐TITE configuration enabling borrowing/clustering with p_hetero = 0.7 starting at 75% patient accrual exhibits superior dose‐finding accuracy compared to existing methods. [ABSTRACT FROM AUTHOR]

Traditional approaches in dose-finding trials, such as the continual reassessment method, focus on identifying the maximum tolerated dose. In contemporary early-phase dose-finding trials, especially in oncology with targeted agents or immunotherapy, a more relevant aim is to identify the lowest dose level that maximises efficacy whilst remaining tolerable. Backfilling , defined as the practice of assigning patients to dose levels lower than the current highest tolerated dose, has been proposed to gather additional pharmacokinetic, pharmacodynamic and biomarker data to recommend the most appropriate dose to carry forward for subsequent studies. The first formal framework [5] for backfilling proposed randomising backfill patients with equal probability among those doses below the dose level where the study is currently at. Here, we propose to use Bayesian response-adaptive randomisation to backfill patients. This patient-oriented approach to backfilling aims to allocate more patients to dose levels in the backfill set with higher expected efficacy based on emerging data. The backfill set constitutes of the doses below the dose the dose-finding algorithm is at. At study completion, collective patient data inform the dose-response curve, suggesting an optimal dose level balancing toxicity and efficacy. Our simulation study across diverse clinical trial settings demonstrates that a backfilling strategy using Bayesian response-adaptive randomisation can result in a patient-oriented patient assignment procedure which simultaneously enhances the likelihood of correctly identifying the most appropriate dose level. This contribution offers a methodological framework and practical implementation for patient-oriented backfilling, encompassing design and analysis considerations in early-phase trials. [ABSTRACT FROM AUTHOR]

Abstract Background The continual reassessment method (CRM) identifies the maximum tolerated dose (MTD) more efficiently and identifies the true MTD more frequently compared to standard methods such as the 3 + 3 method. An initial estimate of the dose-toxicity relationship (prior skeleton) is required, and there is limited guidance on how to select this. Previously, we compared the CRM with six different skeletons to the 3 + 3 method by conducting post-hoc analysis on a phase 1 oncology study (AZD3514), each CRM model reduced the number of patients allocated to suboptimal and toxic doses. This manuscript extends this work by assessing the ability of the 3 + 3 method and the CRM with different skeletons in determining the true MTD of various “true” dose-toxicity relationships. Methods One thousand studies were simulated for each “true” dose toxicity relationship considered, four were based on clinical trial data (AZD3514, AZD1208, AZD1480, AZD4877), and four were theoretical. The 3 + 3 method and 2-stage extended CRM with six skeletons were applied to identify the MTD, where the true MTD was considered as the largest dose where the probability of experiencing a dose limiting toxicity (DLT) is ≤33%. Results For every true dose-toxicity relationship, the CRM selected the MTD that matched the true MTD in a higher proportion of studies compared to the 3 + 3 method. The CRM overestimated the MTD in a higher proportion of simulations compared to the 3 + 3 method. The proportion of studies where the correct MTD was selected varied considerably between skeletons. For some true dose-toxicity relationships, some skeletons identified the true MTD in a higher proportion of scenarios compared to the skeleton that matched the true dose-toxicity relationship. Conclusion Through simulation, the CRM generally outperformed the 3 + 3 method for the clinical and theoretical true dose-toxicity relationships. It was observed that accurate estimates of the true skeleton do not always outperform a generic skeleton, therefore the application of wide confidence intervals may enable a generic skeleton to be used. Further work is needed to determine the optimum skeleton.

Abstract Background Scheduled cesarean section is routinely performed under spinal anesthesia using hyperbaric bupivacaine. The current study was undertaken to determine the clinically relevant 95% effective dose of intrathecal 2% hyperbaric prilocaine co-administered with sufentanil for scheduled cesarean section, using continual reassessment method. Methods We conducted a dose-response, prospective, double-blinded study to determine the ED95 values of intrathecal hyperbaric prilocaine used with 2,5 mcg of sufentanil and 100 mcg of morphine for cesarean delivery. Each parturient enrolled in the study received an intrathecal dose of hyperbaric prilocaine determined by the CRM and the success or failure of the block was assessed as being the primary endpoint. Results The doses given for each cohort varied from 35 to 50 mg of HP, according to the CRM, with a final ED95 lying between 45 and 50 mg of Prilocaine after completion of the 10 cohorts. Few side effects were reported and patients were globally satisfied. Conclusions The ED95 of intrathecal hyperbaric prilocaine with sufentanil 2.5 μg and morphine 100 μg for elective cesarean delivery was found to be between 45 and 50 mg. It may be an interesting alternative to other long-lasting local anesthetics in this context. Trial registration The study was registered on January 30, 2017 – retrospectively registered – and results posted at the public database clinicaltrials.gov ( NCT03036384 ).

Lung cancer is the leading cause of cancer mortality worldwide and most patients are unsuitable for ‘gold standard’ treatment, which is concurrent chemoradiotherapy. CONCORDE is a platform study seeking to establish the toxicity profiles of multiple novel radiosensitisers targeting DNA repair proteins in patients treated with sequential chemoradiotherapy. Time-to-event continual reassessment will facilitate efficient dose-finding.

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

Background: Determining the maximum tolerated dose (MTD) remains the primary objective for the majority of dose-finding oncology trials. Whilst MTD determination often relies upon clinicians to identify dose-limiting toxicities (DLTs) experienced by patients during the trial, research suggests that clinicians may underreport patient's adverse events. Therefore, contemporary practice may be exposed to recommending intolerable doses to patients for further investigation in subsequent trials. There is increasing interest in patients self-assessing their own symptoms using patient-reported outcomes (PROs) in dose-finding trials., Design: We present Utility-PRO-Continual Reassessment Method (U-PRO-CRM), a novel trial design which simultaneously uses clinician-rated and patient-rated DLTs (Clinician-DLTs and Patient-DLTs, respectively) to make dose (de-)escalation decisions and to recommend an MTD. U-PRO-CRM contains the published PRO-CRM as a special case and provides greater flexibility to trade-off the rate of Patient-DLTs and Clinician-DLTs to find an optimal dose. We present simulation results for U-PRO-CRM., Results: For specified trade-offs between Clinician-DLT and Patient-DLT rate, U-PRO-CRM outperforms the PRO-CRM design by identifying the true MTD more often. In the special case where U-PRO-CRM generalises to PRO-CRM, U-PRO-CRM performs as well as its published counterpart. U-PRO-CRM minimises the number of patients overdosed whilst maintaining a similar proportion of patients allocated to the true MTD., Conclusions: By using a utility-based dose selection approach, U-PRO-CRM offers the flexibility to define a trade-off between the risk of patient-rated and clinician-rated DLTs for an optimal dose. Patient-centric dose-finding strategies, which integrate PROs, are poised to assume an ever more pivotal role in significantly advancing our understanding of treatment tolerability. This bears significant implications in shaping the future landscape of early-phase trials., Competing Interests: Disclosure The authors have declared no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Many Phase I trial designs have been developed to improve upon the standard 3 + 3 design. These designs can be classified as long-memory designs, for example, the continual reassessment method (CRM), and short-memory designs such as the modified toxicity probability interval (mTPI) design. Long-term memory designs use all data but their performance can be negatively affected by the model misspecification. Short-term memory designs only use data at the current dose and might lose efficiency as a result. To overcome these issues, we propose a regularized CRM (rCRM). The rCRM offers a trade-off between long-term memory and short-term memory methods. The rCRM gives more weight to data obtained at the doses with the estimated probability of toxicity closer to the target toxicity rate. The addition of a regularization term has an effect of shrinking the dimension of the model and leads to improved performance of the 2-parameter CRM. The rCRM is a good design choice to guide assignments in an expansion cohort phase of a dose-finding trial since dose assignments do not seem to change as often as in corresponding CRMs. [ABSTRACT FROM AUTHOR]

We propose a new design of the continual reassessment method and systematically evaluate its performance on certain operating measures to satisfy the requirements of collective and individual ethics. Simulation results are derived to compare the new design with currently available designs on criteria BEARS: Benchmark, Efficiency, Accuracy, Reliability, Safety. Overall, the new design performs well and safely avoids toxic doses (hence safeguarding individual ethics) while reliably identifying the maximum tolerated dose (hence protecting collective ethics). [ABSTRACT FROM AUTHOR]

Introduction: In some phase I trial settings, there is uncertainty in assessing whether a given patient meets the criteria for dose-limiting toxicity. Methods: We present a design which accommodates dose-limiting toxicity outcomes that are assessed with uncertainty for some patients. Our approach could be utilized in many available phase I trial designs, but we focus on the continual reassessment method due to its popularity. We assume that for some patients, instead of the usual binary dose-limiting toxicity outcome, we observe a physician-assessed probability of dose-limiting toxicity specific to a given patient. Data augmentation is used to estimate the posterior probabilities of dose-limiting toxicity at each dose level based on both the fully observed and partially observed patient outcomes. A simulation study is used to assess the performance of the design relative to using the continual reassessment method on the true dose-limiting toxicity outcomes (available in simulation setting only) and relative to simple thresholding approaches. Results: Among the designs utilizing the partially observed outcomes, our proposed design has the best overall performance in terms of probability of selecting correct maximum tolerated dose and number of patients treated at the maximum tolerated dose. Conclusion: Incorporating uncertainty in dose-limiting toxicity assessment can improve the performance of the continual reassessment method design. [ABSTRACT FROM AUTHOR]

Model-based dose escalation designs have gained increasing interest due to the need for more efficient and informative Phase I trials. The wide-spread implementation of such designs has been hindered by the need for either licensing specialized commercial software or programming the design and simulations from scratch for each project. The R package crmPack provides a simple and unified object-oriented framework for model-based dose escalation designs. This enables the standard use of such designs, while being able to flexibly adapt and extend them. The framework comprises classes and methods for the data structure including the dose grid, statistical models including prior specification, rules for maximum increments, next best dose, and adaptive stopping and cohort sizes. In addition to multiple modified classic continual reassessment method and escalation with overdose control designs with possibly advanced prior specifications (e.g., minimal informative and mixture priors), crmPack currently features dual-endpoint (safety and biomarker) designs and two-part designs. Optional assignment of a small number of patients in each cohort to placebo instead of treatment enables the use in trials outside oncology.

Abstract 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.

Abstract Background The main purpose of dose-finding studies in Phase I trial is to estimate maximum tolerated dose (MTD), which is the maximum test dose that can be assigned with an acceptable level of toxicity. Existing methods developed for single-agent dose-finding assume that the dose-toxicity relationship follows a specific parametric potency curve. This assumption may lead to bias and unsafe dose escalations due to the misspecification of parametric curve. Methods This paper relaxes the parametric assumption of dose-toxicity relationship by imposing a Dirichlet process prior on unknown dose-toxicity curve. A hybrid algorithm combining the Gibbs sampler and adaptive rejection Metropolis sampling (ARMS) algorithm is developed to estimate the dose-toxicity curve, and a two-stage Bayesian nonparametric adaptive design is presented to estimate MTD. Results For comparison, we consider two classical continual reassessment methods (CRMs) (i.e., logistic and power models). Numerical results show the flexibility of the proposed method for single-agent dose-finding trials, and the proposed method behaves better than two classical CRMs under our considered scenarios. Conclusions The proposed dose-finding procedure is model-free and robust, and behaves satisfactorily even in small sample cases.

The continual reassessment method (CRM) is a well‐known design for dose‐finding trials with the goal of estimating the maximum tolerated dose (MTD), the dose with a given probability of toxicity. The standard assumption is that the probability of toxicity monotonically increases with dose. We show that the CRM can still be consistent and correctly identify the MTD even when the dose‐toxicity curve is not monotone as long as there is monotonicity of the true toxicity probabilities right below and right above the true MTD. In the case of multiple therapies, where it is unclear how to order combinations of dose levels of multiple therapies, our findings provide insight into the performance of the partial order CRM (POCRM). To select the correct dose combination at the end of a trial, the POCRM does not have to select a monotone ordering of drug combinations. We illustrate the connection between our results for the CRM with a nonmonotone dose‐toxicity curve and the POCRM via simulations. [ABSTRACT FROM AUTHOR]

Little has been published in terms of dose-finding methodology in virology. Aside from a few papers focusing on HIV, the considerable progress in dose-finding methodology of the last 25 years has focused almost entirely on oncology. While adverse reactions to cytotoxic drugs may be life threatening, for anti-viral agents we anticipate something different: side effects that provoke the cessation of treatment. This would correspond to treatment failure. On the other hand, success would not be yes/no but would correspond to a range of responses, from small, no more than say 20% reduction in viral load to the complete elimination of the virus. Less than total success matters since this may allow the patient to achieve immune-mediated clearance. The motivation for this article is an upcoming dose-finding trial in chronic norovirus infection. We propose a novel methodology whose goal is twofold: first, to identify the dose that provides the most favorable distribution of treatment outcomes, and, second, to do this in a way that maximizes the treatment benefit for the patients included in the study. [ABSTRACT FROM AUTHOR]

Background: The continual reassessment method (CRM) identifies the maximum tolerated dose (MTD) more efficiently and identifies the true MTD more frequently compared to standard methods such as the 3 + 3 method. An initial estimate of the dose-toxicity relationship (prior skeleton) is required, and there is limited guidance on how to select this. Previously, we compared the CRM with six different skeletons to the 3 + 3 method by conducting post-hoc analysis on a phase 1 oncology study (AZD3514), each CRM model reduced the number of patients allocated to suboptimal and toxic doses. This manuscript extends this work by assessing the ability of the 3 + 3 method and the CRM with different skeletons in determining the true MTD of various "true" dose-toxicity relationships.Methods: One thousand studies were simulated for each "true" dose toxicity relationship considered, four were based on clinical trial data (AZD3514, AZD1208, AZD1480, AZD4877), and four were theoretical. The 3 + 3 method and 2-stage extended CRM with six skeletons were applied to identify the MTD, where the true MTD was considered as the largest dose where the probability of experiencing a dose limiting toxicity (DLT) is ≤33%.Results: For every true dose-toxicity relationship, the CRM selected the MTD that matched the true MTD in a higher proportion of studies compared to the 3 + 3 method. The CRM overestimated the MTD in a higher proportion of simulations compared to the 3 + 3 method. The proportion of studies where the correct MTD was selected varied considerably between skeletons. For some true dose-toxicity relationships, some skeletons identified the true MTD in a higher proportion of scenarios compared to the skeleton that matched the true dose-toxicity relationship.Conclusion: Through simulation, the CRM generally outperformed the 3 + 3 method for the clinical and theoretical true dose-toxicity relationships. It was observed that accurate estimates of the true skeleton do not always outperform a generic skeleton, therefore the application of wide confidence intervals may enable a generic skeleton to be used. Further work is needed to determine the optimum skeleton. [ABSTRACT FROM AUTHOR]

Late-onset (LO) toxicities often arise in the new era of phase I oncology dose-finding trials with targeted agents or immunotherapies. The current LO toxicities modelling is often formulated in a weighted likelihood framework, where the time-to-event continual reassessment method (TITE-CRM) is commonly used. The TITE-CRM uses the patient exposure time as a weight for the censored observation, while there is large uncertainty on which weight function to be used. As an alternative, the fractional scheme formulates an efficient and robust paradigm to address LO toxicity issues in dose finding. We review the fractional continual reassessment method (fCRM) and compare its operating characteristics with those of the TITE-CRM as well as other competitive designs via extensive simulation studies based on both the fixed and randomly generated scenarios. The fCRM is shown to possess desirable operating characteristics in identifying the maximum tolerated dose (MTD) and deliver competitive performances in comparison with other designs. It provides an alternative efficient and robust paradigm for interpreting and addressing LO toxicities in the new era of phase I dose-finding trials in precision oncology. A real trial example is used to illustrate the practical use of the fCRM design.

Abstract 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.

There is a long literature on bias in maximum likelihood estimators. Here we demonstrate that adaptive dose-finding procedures (such as Continual Reassessment Methods, Up-and-Down and Interval Designs) themselves induce bias. In particular, with Bernoulli responses and dose assignments that depend on prior responses, we provide an explicit formula for the bias of observed response rates. We illustrate the patterns of bias for designs that aim to concentrate dose allocations around a target dose, which represents a specific quantile of a cumulative response-threshold distribution. For such designs, bias tends to be positive above the target dose and negative below it. To our knowledge, this property of dose-finding designs has not previously been recognized by design developers. We discuss the implications of this bias and suggest a simple shrinkage mitigation formula that improves estimation at doses away from the target. [ABSTRACT FROM AUTHOR]

This article 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. In fact, balancing the trade‐off between how accurately the MTD can be estimated and how many patients would experience adverse events is one of the primary challenges in phase I studies. Motivated by recent discussions in the theory of estimation in restricted parameter spaces, we propose a criterion that allows to balance these explicitly. The criterion requires a specification of one additional parameter only that has a simple and intuitive interpretation. We incorporate the proposed criterion into the one‐parameter Bayesian continual reassessment method and show, using simulations, that it can result in similar accuracy on average as the original design, but with fewer toxic responses on average. A comparison with other model‐based dose‐escalation designs, such as escalation with overdose control and its modifications, demonstrates that the proposed design can result in either the same mean accuracy as alternatives but fewer toxic responses or in a higher mean accuracy but the same number of toxic responses. Therefore, the proposed design can provide a better trade‐off between the accuracy and the number of patients experiencing adverse events, making the design a more ethical alternative over some of the existing methods for phase I trials. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Background For a clinical trials unit to run its first model-based, phase I trial, the statistician, chief investigator, and trial manager must all acquire a new set of skills. These trials also require a different approach to funding and data collection. Challenges and discussion From the statisticians’ viewpoint, we highlight what is needed to move from running rule-based, early-phase trials to running a model-based phase I study as we experienced it in our trials unit located in the United Kingdom. Our example is CHARIOT, a dose-finding trial using the time-to-event continual reassessment method. It consists of three stages and aims to discover the maximum tolerated dose of the combination of radiotherapy, chemotherapy, and the ataxia telangiectasia mutated Rad3-related inhibitor M6620 (previously known as VX-970) in patients with oesophageal cancer. We present the challenges we faced in designing this trial and how we overcame them as a way of demystifying the conduct of a model-based trial in a grant-funded clinical trials unit. Conclusions Although we appreciate that undertaking model-based trials requires additional time and effort, they are feasible to implement and, once suitable tools such as guiding publications and document templates become available, the design and set-up process will be easier and more efficient.

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 <1% of the total dose assignments made during the simulation study. Similar findings are reported in simulations across 100 randomly generated dose-toxicity scenarios from a recently proposed family of curves. 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. [ABSTRACT FROM AUTHOR]

This paper proposes a phase-I clinical trial design that uses ordinal toxicity to locate group-specific doses when groups are partially or completely ordered prior to the start of the trial. There has been previous work on dose-finding for groups and on dose-finding with ordinal toxicity but a solution to the problem of dose-finding for groups with ordinal toxicity has not been proposed. Simulations compared the proposed method against two methods; one that uses ordinal toxicity but does not use group information and one that uses group information but does not use ordinal toxicity. One issue with the first method is the potential for reversals, when the recommended dose for a more sensitive group is higher than the recommended dose for a less sensitive group. The proposed method avoids reversals, allocates patients to optimal doses more frequently during the trial, and selects optimal doses more frequently at the end of the trial. [ABSTRACT FROM AUTHOR]

• We present a new SAS macro, CRM2DIM, to run a phase I dose-finding trial for two-drug combination and to perform simulation using the dual-agent Bayesian Continual Reassessment Method. • The SAS macro CRM2DIM includes several features such as the use of a start-up, the use of a log-linear (2–3 parameters) and a standard logistic regression (3–4 parameters) models, and the possibility to incorporate historical data via the power approach. • The macro is flexible and many extensions are possible, for example, any desirable working model can be used. This flexibility is due in part to the use of the SAS proc MCMC. • To our knowledge, this is the first SAS macro implementing a dose-finding design for two agents. Background and objective: The continual reassessment method (CRM) is a model-based dose-finding design for single-agent phase I oncology trials. With the advance of targeted therapies in oncology, more and more phase I trials investigate drug combinations rather than a single agent in order to find one or more maximum tolerated dose combinations. Several designs have been proposed for such dose-finding trials but only a few software packages are available to implement them. One of the designs is the two-dimensional Bayesian CRM proposed by Wang and Ivanova. Our goal was to provide an easy-to-use program to implement this design. Methods: We developed a new SAS macro, CRM2DIM, for implementing this design. This macro can be used to run a phase I dose-finding trial for two-drug combination and to perform simulations. Results: We describe the program with its different features, including the possibility of running an initial design (start-up rule), the possibility of incorporating historical data, and the choice of using either a power or a logistic regression model with or without interaction term. We illustrate our program by presenting simulation results and by a hypothetical trial example. Conclusions: The CRM2DIM macro provides a SAS implementation of the two-dimensional Bayesian CRM for dual-agent phase I oncology trials. It is an easy-to-use program that includes many useful features and provides statisticians involved in the early phases of development a new tool for designing dual-agent phase I oncology trials. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: With the advances in the discovery of molecular targets, there is increasing interest in evaluating targeted therapies for disease subtypes characterized by certain biomarkers. Patients with a certain biomarker could potentially benefit from different experimental drugs, and, therefore, evaluating the relative efficacy of these drugs is an important objective. We consider the design of an early phase platform trial where multiple therapies are evaluated in patients with different biomarkers, with the objective of identifing the best drug at an efficacious and safe dose for a given disease subtype. We use the continual reassessment method to estimate the maximum tolerated dose of a drug and adopt hierarchical Bayesian modelling to estimate the efficacy of a drug administered at multiple doses. Using the continual reassessment method and hierarchical Bayesian modelling as the basis of inference, we propose various algorithms that prescribe the drug–dose for the patients using adaptive randomization. We demonstrate that adaptive randomization puts more patients at the right drug and dose on average than does balanced randomization, with slightly larger variability in distribution, and has no effect on the accuracy of drug–dose selection. Moreover the simulations show advantages of hierarchical Bayesian modelling over the beta–binomial model in scenarios with relatively flat or partially flat dose–efficacy relationships. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In view of the impossibility theorem of Azriel et al., the Maximum Tolerated Dose (MTD), as currently defined in Phase I and Phase I/II trials, cannot be consistently estimated without making some very strong parametric assumptions. Experimentation carried out at a single dose will fail to provide a consistent estimator of the MTD. We require that information be obtained on a subset of the doses which we describe as the ‘minimal set’. We provide a definition of this set and study its role in semiparametric inference concerning the MTD. Focusing inference on the minimal set, and only indirectly on the MTD, has important consequences. Among these is consistency of estimators for both the minimal set and the MTD, in completely general conditions. Even when the goal is only to estimate the MTD, we do no worse, and often better than other methods and without making any sacrifice in terms of safety for the patient. [ABSTRACT FROM AUTHOR]

Background: Dose-finding studies in anaesthesiology aim to target the effective dose (ED) of an anaesthetic agent in a specific population. The common dose-finding designs used are the up and down method (UDM), the biased-coin up and down (BCD), and the continual reassessment method (CRM). Although the advantages of CRM over the UDM and BCD methods have been described in the statistical literature in terms of precision and direct estimation of ED, CRM may also offer attractive properties from an ethical point of view., Methods: Based on Monte Carlo simulations, this article aims to compare the three methods with regard to 1) their ability to find as close an estimate as possible for the ED 95 or ED 90 and 2) the total number of patients needed to treat and the number of failures., Results: In contrast to BCD and UDM, CRM does find an estimate for ED 95 and ED 90 . UDM underestimates both ED 95 and ED 90 . BCD is close to the targeted EDs when the starting dose does not exceed the ED of interest, otherwise it overestimates it. CRM with cohorts of two patients is closest to the ED of interest independently of the starting doses. CRM requires between 20 and 50 observations, UDM should include 90 patients, and BCD 100 or 60 observations. Lastly, CRM is associated with fewer failures, compared with BCD and UDM., Conclusions: Based on Monte Carlo simulations, our work suggests that the UDM is not an adequate dose-finding method because it underestimates the ED of interest. Compared with BCD, CRM offers the advantages of being more efficient, requires fewer patients to be included, and is associated with fewer failures., (© 2023 The Author(s).)

The primary goal of a phase I clinical trial in oncology is to evaluate the safety of a novel treatment and identify the maximum tolerated dose, defined as the maximum dose with a toxicity rate below some pre-specified threshold. Researchers are often interested in evaluating the performance of a novel treatment in multiple patient populations, which may require multiple phase I trials if the treatment is to be used with background standard-of-care that varies by population. An alternate approach is to run parallel trials but combine the data through a hierarchical model that allows for a different maximum tolerated dose in each population but shares information across populations to achieve a more accurate estimate of the maximum tolerated dose. In this manuscript, we discuss hierarchical extensions of three commonly used models for the dose-toxicity relationship in phase I oncology trials. We then propose three dose-finding guidelines for phase I oncology trials using hierarchical modeling. The proposed guidelines allow us to fully realize the benefits of hierarchical modeling while achieving a similar toxicity profile to standard phase I designs. Finally, we evaluate the operating characteristics of a phase I clinical trial using the proposed hierarchical models and dose-finding guidelines by simulation. Our simulation results suggest that incorporating hierarchical modeling in phase I dose-escalation studies will increase the probability of correctly identifying the maximum tolerated dose and the number of patients treated at the maximum tolerated dose, while decreasing the rate of dose-limiting toxicities and number of patients treated above the maximum tolerated dose, in most cases. [ABSTRACT FROM AUTHOR]

Abstract 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. [ABSTRACT FROM AUTHOR]

Summary: In contrast with typical Phase III clinical trials, there is little existing methodology for determining the appropriate numbers of patients to enroll in adaptive Phase I trials. And, as stated by Dennis Lindley in a more general context, “[t]he simple practical question of ‘What size of sample should I take’ is often posed to a statistician, and it is a question that is embarrassingly difficult to answer.” Historically, simulation has been the primary option for determining sample sizes for adaptive Phase I trials, and although useful, can be problematic and time‐consuming when a sample size is needed relatively quickly. We propose a computationally fast and simple approach that uses Beta distributions to approximate the posterior distributions of DLT rates of each dose and determines an appropriate sample size through posterior coverage rates. We provide sample sizes produced by our methods for a vast number of realistic Phase I trial settings and demonstrate that our sample sizes are generally larger than those produced by a competing approach that is based upon the nonparametric optimal design. [ABSTRACT FROM AUTHOR]

Traditionally, Phase I oncology trials evaluate the safety profile of a novel agent and identify a maximum tolerable dose based on toxicity alone. With the development of biologically targeted agents, investigators believe the efficacy of a novel agent may plateau or diminish before reaching the maximum tolerable dose while toxicity continues to increase. This motivates dose-finding based on the simultaneous evaluation of toxicity and efficacy. Previously, we investigated hierarchical modeling in the context of Phase I dose-escalation studies for multiple populations and found borrowing strength across populations improved operating characteristics. In this article, we discuss three hierarchical extensions to commonly used probability models for efficacy and toxicity in Phase I-II trials and adapt our previously proposed dose-finding algorithm for multiple populations to this setting. First, we consider both parametric and non-parametric bivariate models for binary outcomes and, in addition, we consider an under-parameterized model that combines toxicity and efficacy into a single trinary outcome. Our simulation results indicate hierarchical modeling increases the probability of correctly identifying the optimal dose and increases the average number of patients treated at the optimal dose, with the under-parameterized hierarchical model displaying desirable and robust operating characteristics. [ABSTRACT FROM AUTHOR]

Background: For a clinical trials unit to run its first model-based, phase I trial, the statistician, chief investigator, and trial manager must all acquire a new set of skills. These trials also require a different approach to funding and data collection.Challenges and Discussion: From the statisticians' viewpoint, we highlight what is needed to move from running rule-based, early-phase trials to running a model-based phase I study as we experienced it in our trials unit located in the United Kingdom. Our example is CHARIOT, a dose-finding trial using the time-to-event continual reassessment method. It consists of three stages and aims to discover the maximum tolerated dose of the combination of radiotherapy, chemotherapy, and the ataxia telangiectasia mutated Rad3-related inhibitor M6620 (previously known as VX-970) in patients with oesophageal cancer. We present the challenges we faced in designing this trial and how we overcame them as a way of demystifying the conduct of a model-based trial in a grant-funded clinical trials unit.Conclusions: Although we appreciate that undertaking model-based trials requires additional time and effort, they are feasible to implement and, once suitable tools such as guiding publications and document templates become available, the design and set-up process will be easier and more efficient. [ABSTRACT FROM AUTHOR]

Background: Phase I trials are designed to determine the safety, tolerability, and recommended phase 2 dose of therapeutic agents for subsequent testing. The dose-finding paradigm has thus traditionally focused on identifying the maximum tolerable dose of an agent or combination therapy under the assumption that there is a non-decreasing relationship between dose-toxicity and dose-efficacy. The dose is typically determined based on the probability of severe toxicity observed during the first treatment cycle. A novel endpoint, the total toxicity profile, was previously developed to account for the multiple toxicity types and grades experienced in the first cycle. More recently, this was extended to a repeated measures design based on the total toxicity profile to account for longitudinal toxicities over multiple treatment cycles in the absence of within-patient correlation. Methods: In this work, we propose to extend the design in the presence of within-patient correlation. Furthermore, we provide a framework to detect a toxicity time trend (toxicity increasing, decreasing, or stable) over multiple treatment cycles. We utilize a linear mixed model in the Bayesian framework, with the addition of Bayesian risk functions for decision-making in dose assignment. Results: The performance of this design was evaluated using simulation studies and real data from a phase I trial. We demonstrated that using available toxicity data from all cycles of treatment improves the accuracy of maximum tolerated dose identification and allows for the detection of a time trend. The performance is consistent regardless of the strength of the within-patient correlation. In addition, the use of a quasi-continuous total toxicity profile score significantly increased the power to detect time trends compared to when binary data only were used. Conclusion: The increased interest in molecularly targeted agents and immunotherapies in oncology necessitates innovative phase I study designs. Our proposed framework provides a tool to tackle some of the challenges presented by these novel agents, specifically through the ability to understand patterns of toxicity over time, which is important in the cases of cumulative or late toxicities. [ABSTRACT FROM AUTHOR]