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In the last few years, numerous novel designs have been proposed to improve the efficiency and accuracy of phase I trials to identify the maximum-tolerated dose (MTD) or the optimal biological dose (OBD) for noncytotoxic agents. However, the conventional 3+3 approach, known for its and poor performance, continues to be an attractive choice for many trials despite these alternative suggestions. The article seeks to underscore the importance of moving beyond the 3+3 design by highlighting a different key element in trial design: the estimation of sample size and its crucial role in predicting toxicity and determining the MTD. We use simulation studies to compare the performance of the most used phase I approaches: 3+3, Continual Reassessment Method (CRM), Keyboard and Bayesian Optimal Interval (BOIN) designs regarding three key operating characteristics: the percentage of correct selection of the true MTD, the average number of patients allocated per dose level, and the average total sample size. The simulation results consistently show that the 3+3 algorithm underperforms in comparison to model-based and model-assisted designs across all scenarios and metrics. The 3+3 method yields significantly lower (up to three times) probabilities in identifying the correct MTD, often selecting doses one or even two levels below the actual MTD. The 3+3 design allocates significantly fewer patients at the true MTD, assigns higher numbers to lower dose levels, and rarely explores doses above the target dose-limiting toxicity (DLT) rate. The overall performance of the 3+3 method is suboptimal, with a high level of unexplained uncertainty and significant implications for accurately determining the MTD. While the primary focus of the article is to demonstrate the limitations of the 3+3 algorithm, the question remains about the preferred alternative approach. The intention is not to definitively recommend one model-based or model-assisted method over others, as their performance can vary based on parameters and model specifications. However, the presented results indicate that the CRM, Keyboard, and BOIN designs consistently outperform the 3+3 and offer improved efficiency and precision in determining the MTD, which is crucial in early-phase clinical trials.

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Allergen immunotherapy (AIT) is the only causal therapy for allergic diseases and therefore particularly important. Allergen preparations have been classified as medicinal products since 1989 (Directive 89/342/EEC) and were taken over into Directive 2001/83/EC in 2001. In addition, in 2008 the Therapy Allergen Ordinance (TAO) came into force to stricter regulate the exception for named patient products (NPP) by exclusion of common therapy allergens from the exception to be marketed as NPP. The TAO regulates the requirements for testing safety and efficacy for these common therapy allergens. Due to the long transitional provisions, the last deadlines for solving clinical shortcomings will end in 2026. The advantage of this regulation is that the market for common allergens has been cleared of products without proof of efficacy, and new preparations with an optimal dose range are developed through dose-finding studies. The demand for long-term pediatric studies has been outlined by the standard Pediatric Investigation Plan (PIP) on allergen products from the Pediatric Committee of the EMA (PDCO). This is particularly problematic, as it is foreseeable that recruitment of patients will be limited and ethical problems arise from the prolonged use of placebo. Furthermore, many newly approved preparations will not be used in pediatrics for the foreseeable future, as no marketing authorization has yet been granted for this age group. This will result in a serious supply gap for children.

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In clinical trials, the comparison of two different populations is a common problem. Nonlinear (parametric) regression models are commonly used to describe the relationship between covariates, such as concentration or dose, and a response variable in the two groups. In some situations, it is reasonable to assume some model parameters to be the same, for instance, the placebo effect or the maximum treatment effect. In this paper, we develop a (parametric) bootstrap test to establish the similarity of two regression curves sharing some common parameters. We show by theoretical arguments and by means of a simulation study that the new test controls its significance level and achieves a reasonable power. Moreover, it is demonstrated that under the assumption of common parameters, a considerably more powerful test can be constructed compared with the test that does not use this assumption. Finally, we illustrate the potential applications of the new methodology by a clinical trial example.

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Phase II studies on allergen immunotherapy (AIT) should define the dose with the best balance between efficacy and safety ("optimal dose"). Their key role is based on dose selection for subsequent pivotal studies (phase III, field studies). Since products for AIT differ in composition and unit definitions, phase II trials are mandatory for new products and preparations being developed according to the German Therapy Allergen Ordinance ("Therapie-Allergeneverordnung", TAV) due to current EMA guidelines since 2009. The latter permit various in-vivo models and endpoints for phase II studies, e.g., AIT-induced changes in skin test, nasal, conjunctival or bronchial provocation, or in exposure chamber or field trials. Selection and graduation of the doses, minimization of placebo effects, and sufficient numbers of patients are a challenge. Effort, required time, and costs are important variables for the initiators of phase II trials. Risks are characterized by e.g., a) too small doses without relevant differences compared to placebo, b) missing true dose-response relationships, c) strong placebo effect and consequently small "therapeutic window", d) large heterogeneity and missing distinct differences (compared to placebo), e) too small effects in field studies due to low allergen exposure, f) missing dose-related increase (in case of too high doses). In the view of the Paul-Ehrlich-Institute, the unambiguous phase II trials with TAV products performed until today were not able to confirm the marketed doses for AIT. Regardless of the utilized model, more raw and single data should illustrate the individual outcome of AIT during phase II trials, facilitating an improved and more intuitive interpretation of the data (placebo effects? scattering?). In the medium term, evidence regarding AIT efficacy will considerably increase due to phase II trials as a prerequisite for subsequent phase III field studies. This affects all manufacturers offering AIT products in Germany and Europe.

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Characterizing an appropriate dose-response relationship and identifying the right dose in a clinical trial are two main goals of early drug-development. MCP-Mod is one of the pioneer approaches developed within the last 10 years that combines the modeling techniques with multiple comparison procedures to address the above goals in clinical drug development. The MCP-Mod approach begins with a set of potential dose-response models, tests for a significant dose-response effect (proof of concept, PoC) using multiple linear contrasts tests and selects the "best" model among those with a significant contrast test. A disadvantage of the method is that the parameter values of the candidate models need to be fixed a priori for the contrasts tests. This may lead to a loss in power and unreliable model selection. For this reason, several variations of the MCP-Mod approach and a hierarchical model selection approach have been suggested where the parameter values need not be fixed in the proof of concept testing step and can be estimated after the model selection step. This paper provides a numerical comparison of the different MCP-Mod variants and the hierarchical model selection approach with regard to their ability of detecting the dose-response trend, their potential to select the correct model and their accuracy in estimating the dose response shape and minimum effective dose. Additionally, as one of the approaches is based on two-sided model comparisons only, we make it more consistent with the common goals of a PoC study, by extending it to one-sided comparisons between the constant and alternative candidate models in the proof of concept step.

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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.

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Background. Absent adaptive, individualized dose-finding in early-phase oncology trials, subsequent 'confirmatory' Phase III trials risk suboptimal dosing, with resulting loss of statistical power and reduced probability of technical success for the investigational therapy. While progress has been made toward explicitly adaptive dose-finding and quantitative modeling of dose-response relationships, most such work continues to be organized around a concept of 'the' maximum tolerated dose (MTD). The purpose of this paper is to demonstrate concretely how the aim of early-phase trials might be conceived, not as 'dose-finding', but as dose titration algorithm (DTA)-finding. Methods. A Phase I dosing study is simulated, for a notional cytotoxic chemotherapy drug, with neutropenia constituting the critical dose-limiting toxicity. The drug's population pharmacokinetics and myelosuppression dynamics are simulated using published parameter estimates for docetaxel. The amenability of this model to linearization is explored empirically. The properties of a simple DTA targeting neutrophil nadir of 500 cells/mm 3 using a Newton-Raphson heuristic are explored through simulation in 25 simulated study subjects. Results. Individual-level myelosuppression dynamics in the simulation model approximately linearize under simple transformations of neutrophil concentration and drug dose. The simulated dose titration exhibits largely satisfactory convergence, with great variance in individualized optimal dosing. Some titration courses exhibit overshooting. Conclusions. The large inter-individual variability in simulated optimal dosing underscores the need to replace 'the' MTD with an individualized concept of MTD i . To illustrate this principle, the simplest possible DTA capable of realizing such a concept is demonstrated. Qualitative phenomena observed in this demonstration support discussion of the notion of tuning such algorithms. Although here illustrated specifically in relation to cytotoxic chemotherapy, the DTAT principle appears similarly applicable to Phase I studies of cancer immunotherapy and molecularly targeted agents.

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Toxicity probability interval designs have received increasing attention as a dose-finding method in recent years. In this study, we compared the two-stage, likelihood-based continual reassessment method (CRM), modified toxicity probability interval (mTPI), and the Bayesian optimal interval design (BOIN) in order to evaluate each method's performance in dose selection for phase I trials. We use several summary measures to compare the performance of these methods, including percentage of correct selection (PCS) of the true maximum tolerable dose (MTD), allocation of patients to doses at and around the true MTD, and an accuracy index. This index is an efficiency measure that describes the entire distribution of MTD selection and patient allocation by taking into account the distance between the true probability of toxicity at each dose level and the target toxicity rate. The simulation study considered a broad range of toxicity curves and various sample sizes. When considering PCS, we found that CRM outperformed the two competing methods in most scenarios, followed by BOIN, then mTPI. We observed a similar trend when considering the accuracy index for dose allocation, where CRM most often outperformed both mTPI and BOIN. These trends were more pronounced with increasing number of dose levels. Copyright © 2016 John Wiley & Sons, Ltd.

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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. Copyright © 2016 John Wiley & Sons, Ltd.

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Adaptive, model-based, dose-finding methods, such as the continual reassessment method, have been shown to have good operating characteristics. One school of thought argues in favor of the use of parsimonious models, not modeling all aspects of the problem, and using a strict minimum number of parameters. In particular, for the standard situation of a single homogeneous group, it is common to appeal to a one-parameter model. Other authors argue for a more classical approach that models all aspects of the problem. Here, we show that increasing the dimension of the parameter space, in the context of adaptive dose-finding studies, is usually counter productive and, rather than leading to improvements in operating characteristics, the added dimensionality is likely to result in difficulties. Among these are inconsistency of parameter estimates, lack of coherence in escalation or de-escalation, erratic behavior, getting stuck at the wrong level, and, in almost all cases, poorer performance in terms of correct identification of the targeted dose. Our conclusions are based on both theoretical results and simulations. Copyright © 2016 John Wiley & Sons, Ltd.

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We propose a Phase I/II trial design in which subjects with dose-limiting toxicity are not followed for response, leading to three possible outcomes for each subject: dose-limiting toxicity, absence of therapeutic response without dose-limiting toxicity, and presence of therapeutic response without dose-limiting toxicity. We define the latter outcome as a 'success,' and the goal of the trial is to identify the dose with the largest probability of success. This dose is commonly referred to as the most successful dose. We propose a design that accumulates information on subjects with regard to both dose-limiting toxicity and response conditional on no dose-limiting toxicity. Bayesian methods are used to update the estimates of dose-limiting toxicity and response probabilities when each subject is enrolled, and we use these methods to determine the dose level assigned to each subject. Due to the need to explore doses more fully, each subject is not necessarily assigned the current estimate of the most successful dose; our algorithm may instead assign a dose that is in a neighborhood of the current most successful dose. We examine the ability of our design to correctly identify the most successful dose in a variety of settings via simulation and compare the performance of our design to that of competing approaches.

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Here, we developed a new dose-finding method that partitions a cohort of patients based on the number of dose combinations within a prespecified acceptable toxicity range in two-agent combination Phase I trials. In the proposed method, patients in the same cohort are partitioned according to several dose combinations, although most of the existing methods allocate patients in the same cohort according to a single-dose combination. We compared the operating characteristics of the proposed and existing methods through simulation studies.

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The aim of phase I combination dose-finding studies in oncology is to estimate one or several maximum tolerated doses (MTDs) from a set of available dose levels of two or more agents. Combining several agents can indeed increase the overall anti-tumor action but at the same time also increase the toxicity. It is, however, unreasonable to assume the same dose-toxicity relationship for the combination as for the simple addition of each single agent because of a potential antagonist or synergistic effect. Therefore, using single-agent dose-finding methods for combination therapies is not appropriate. In recent years, several authors have proposed novel dose-finding designs for combination studies, which use either algorithm-based or model-based methods. The aim of our work was to compare, via a simulation study, six dose-finding methods for combinations proposed in recent years. We chose eight scenarios that differ in terms of the number and location of the true MTD(s) in the combination space. We then compared the performance of each design in terms of correct combination selection, patient allocation, and mean number of observed toxicities during the trials. Our results showed that the model-based methods performed better than the algorithm-based ones. However, none of the compared model-based designs gave consistently better results than the others.

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The idea of bridging in dose-finding studies is closely linked to the problem of group heterogeneity. There are some distinctive features in the case of bridging which need to be considered if efficient estimation of the maximum tolerated dose (MTD) is to be accomplished. The case of two distinct populations is considered. In the bridging setting we usually have in mind two studies, corresponding to the two populations. In some cases, the first of these studies may have been completed while the second has yet to be initiated. In other cases, the studies take place simultaneously and information can then be shared among the two groups. The methodological problem is how to make most use of the information gained in the first study to help improve efficiency in the second. We describe the models that we can use for the purpose of bridging and study situations in which their use leads to overall improvements in performance as well as cases where there is no gain when compared to carrying out parallel studies. Simulations and an example in pediatric oncology help to provide further insight.

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The majority of methods for the design of phase I trials in oncology are based upon a single course of therapy, yet in actual practice, it may be the case that there is more than one treatment schedule for any given dose. Therefore, the probability of observing a dose-limiting toxicity may depend upon both the total amount of the dose given, as well as the frequency with which it is administered. The objective of the study then becomes to find an acceptable combination of both dose and schedule. Past literature on designing these trials has entailed the assumption that toxicity increases monotonically with both dose and schedule. In this article, we relax this assumption for schedules and present a dose-schedule finding design that can be generalized to situations in which we know the ordering between all schedules and those in which we do not. We present simulation results that compare our method with other suggested dose-schedule finding methodology.

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We develop a novel dose-finding method for two-agent combination phase I trials on the basis of the shrunken predictive probability of toxicity. In this method, a shrinkage logistic regression model that allows distinct shrinkage multipliers for the coefficients of the main effects of two agents and their interaction on the probability of toxicity constructs the toxicity outcome. We also propose dose-escalation/de-escalation decision rules on the basis of the shrunken predictive probability of toxicity. Simulation studies under various patterns of monotonic dose-response relationships for combinations of two agents demonstrated that the proposed method performed no worse than the existing two dose-finding methods we selected.

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