Specifically, Figure 2(b) shows that the i3+3 design possesses almost identical decision tables as the mTPI and mTPI-2 designs, rendering highly similar operating characteristics. Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials. Food and Drug Administration. Usually, massive computer simulations are required to understand the frequentist properties of a Bayesian method, and calibration of the Bayesian method (e.g., calibration of the prior and probability threshold for decision-making) is needed to achieve desirable frequentist performance. Another type of method casts subgroup analysis as a decision problem consisting of two components: (1) inference for differential treatment effects and (2) an action of reporting subgroups. Immuno-Oncology Clinical Trial Development. The ORR in each substudy of a basket trial is compared with a historical control rate, and substudies with promising responses would warrant further investigation or conditional marketing approval. Figure 3. To identify the MTD through a clinical trial, a grid of discrete dose levels is usually pre-specified, and an ethical and efficient statistical design assigns patients sequentially and in cohorts to different doses. Pain; Bone Neoplasms; Neoplasm Metastasis, Thrombocytopenia and Thrombocytopenia Prevention. The development of novel dose-finding designs for Phase I trials has been through a “spiral evolution” that evolves from Class 1 to Class 2 to Class 3, and back to Class 1 but with smarter rules. In effect, the estimated mean response for the current control arm is shrunk toward the overall mean μ across all studies. The standard deviation parameter τ characterizes the degree of shrinkage. At each interim analysis, one can stop accrual for basket j early if Pr(θj > θ0j | data) < pF, where pF is a pre-specified futility stopping threshold, e.g., 0.05. While Bayesian methods are appealing in the design and analysis of clinical trials, they have their own caveats. Such inference, in theory, is more-advanced than rule-based designs, since it borrows information from all doses when making decisions. Types of clinical trials. 2019. Similarly, in the tree model, subgroup-specific treatment effects ηm‘s can be shrunk toward a common value. In either type of seamless trial, an interim go/no-go decision evaluates whether the trial can transition from the earlier phase to the later phase. This often creates added complexity and potential errors in practice. Such trials are referred to as master protocols. Consider a trial with J baskets. It’s no secret that pharmaceutical and clinical trial industries are reluctant to adapt to technological changes. Further, let γj = log[θj / (1 − θj )] − log[θ0j / (1 − θ0j)] be the log-odds of the response rate in basket j after adjusted for the historical control rate. Assuming θ, θH1,…, θHJ are correlated and a priori exchangeable, they can be modeled as random samples from a common population distribution G. For example, if θ is the mean response, one can model. Coronavirus disease 2019 (COVID-19) is an ongoing pandemic affecting more than 200 countries and regions. Efficacy is typically evaluated based on outcomes that can be measured in a relatively short amount of time after treatment, such as tumor shrinkage. The i3+3 design. There are two main Bayesian methods for incorporating historical data. Use of Historical Control Data for Assessing Treatment Effects in Clinical Trials. These issues are likely to impair the validity of the trial, so they require careful consideration by the trial conductor. A new oncology drug usually goes through three phases of clinical trials before it can be approved by regulatory agencies as a commercial product. This article focuses on early-phase trials—Phase I and Phase II—with an emphasis on Bayesian methods. For the former, one can use flexible models to capture the relationship among Y, T, and X, e.g., E(Y|θ) = ⨍(T, X, θ), where ⨍ is a Bayesian additive regression tree model, and θ denotes additional parameters. Conventionally, cancers are categorized based on the anatomic location of the primary tumor (e.g., breast, lung), and clinical trials in oncology are conducted to evaluate a single treatment in a certain cancer type. At the end of the trial, a dose is selected as the MTD, which is defined as the highest dose with DLT probability no more than a pre-specified target θT. Patients will always receive treatment on a clinical trial if an approved treatment already exists. In the future, trials are expected to become more adaptive, embracing modern computing power and adaptive decision-making. Figure 4. Inference of the model parameters can be carried out under the Bayesian framework. The optimal decision can be based on maximizing the posterior expectation of the utility function. where nd and md are the numbers of DLT and non-DLT outcomes at dose d, respectively. Several methods offer value for future efforts. The mTPI design considers a partition of the [0, 1] interval into an equivalence interval (EI) IE = [θT − ϵ1, θT + ϵ2], an underdosing interval (UI) IU = [0, θT − ϵ1), and an overdosing interval (OI) IO, = (θT + ϵ2, 1]. For example, whenever a null hypothesis in a clinical trial is rejected, e.g., a new drug is approved for public use, such approval is associated with a (ideally small) error probability. Consider again the randomized controlled Phase II trial example. It is an exciting era to be a trialist, due to the explosive innovation in the development of novel trial designs. Phase 1 Clinical Trials NEXT Oncology is dedicated to the advancement of Phase 1 cancer research through clinical trials of anticancer agents with the goal of providing innovative developments in cancer treatment. The modified toxicity probability interval (mTPI) design aims to use model-based inference with a rule-based presentation for dose-finding. 300 N. Washington St., Suite 200 Footnotes: § Alvocidib was formerly known as flavopiridol. We encourage you to speak with your doctor to see if a Merck Oncology Clinical Trial may be right for you. Panel (a) shows the resulting posterior distribution of the control ORR with different choices of the power prior distribution. Food and Drug Administration. The following organizations listed in alphabetical order offer free, searchable listings of cancer clinical trials. This feature is crucial in helping resolve some challenges for COVID-era trials, in which unplanned interim analyses may be needed due to the pandemic. Specifically, the power prior distribution for θ is defined as: where α∈[0, 1] controls the weight of the historical data. Congrats! Priors can be constructed to favor parsimonious models. Lastly, a Phase III confirmatory trial assesses the effectiveness of the drug in terms of clinical benefits, such as survival. For example, Phase I/II seamless designs allow a Phase I dose-finding trial and a Phase II trial to be included in a single protocol, while Phase II/III designs combine Phase II and Phase III trials. Let θd denote the probability of DLT at dose d. The CRM models θd with a parametric curve, θd = Ψ (d, α), where Ψ is a parametric function of dose level d and depends on parameters α. Figure 2: (a) Spiral evolution of dose-finding designs (1989—2019). This modeling setup initially appears to be overly simplified. Then, the probability of assigning the patient to treatment t can be proportional to Pr(q̃t > q̃t’ for all t’ ≠ t | x̃, data)α, where α∈[0, 1] is a pre-specified tuning parameter. Generally speaking, there are three classes of decision rules in the literature: Class 1—decision rules that are purely algorithmic on the basis of simple logic to protect patient safety and find the MTD; Class 2—decision rules based on statistical modeling of a dose-response curve; and Class 3—decision rules using curve-free statistical inference and up-and-down rules. A dose-finding design consists of a set of decision rules through which patient enrollments and dose escalations are conducted. In particular, the “objective response” consists of the partial response and complete response based on how much the tumor shrinks after treatment. There is a lot to consider when deciding whether to participate in a clinical trial. 2018. Then, the terminal nodes of define a partition of the covariate space into subspaces, denoted by {1,…,M}. It also defies the common statistical principle of using parametric models or borrowing information when possible. The exploratory trial may also use response-adaptive randomization with a preference to allocate patients to the treatment arms from which they may be more likely to benefit. It is also possible to simultaneously consider several competing models and use Bayesian model selection to pick a winning model. Please note: comment moderation is enabled and may delay your comment. A new application of basket trials is in multiple expansion cohort trials that proceed a dose-finding trial. For Phase II/III designs, the decisions determine whether a Phase II arm can be graduated or dropped and whether a Phase III trial is justified. was proposed as a design that abandons any models and returns to rule-based inference, but with smarter rules. By placing priors on μ and τ, these parameters can be estimated from the data. Lastly, the posterior distribution for the parameter of interest does not have a closed-form expression in many situations, and computationally intensive Monte Carlo methods are necessary for inference. Accelerate your immuno-oncology studies by leveraging scientific and operational expertise. Tianjian Zhou is a postdoctoral scholar in the Department of Public Health Sciences at the University of Chicago. A brief, high-level review of four representative dose-finding designs can be used to explain the spiral evolution. With historical data providing information about the control arm, more resources can be devoted to the new treatment. Any new processes need to be painstakingly assessed to ensure that replacing tried and tested processes doesn’t result in any new risks. Clinical Trials in Oncology The following medical conditions affect the medical, surgical and radiation treatment of tumors, cancerous especially. Figure 3(a) shows this posterior density with different choices of α. Hierarchical Models. For the latter, one can introduce a utility function u(S, θ) for the action of reporting a certain subgroup S, which usually has a preference for a large effect size, a large subgroup size, and a parsimonious description of the subgroup. It is only possible to commit a Type I error when the null is rejected. He led a publication in Nature Methods and invention of a tool called TCGA-Assembler, which has been downloaded more than 10,000 times worldwide. Future trial designs. You have your own personal workspace now. Subgroup analysis can be carried out to inform the design and decision-making of clinical trials. That is, the approval could be false. Seamless designs. British Journal of Cancer). If the prior is not well-constructed, posterior inference may be inefficient. Phase III trials are usually randomized and controlled, and involve large sample sizes with rigorous control of Type I error. Every year hundreds of thousands of volunteers step forward to participate in research. To illustrate the construction of an informative prior, recycle the notation θ and let it denote the parameter of interest of the control arm of the current study. Methods for observational data and missing data, such as inverse probability weighting, can be useful for mitigating these issues. Alternatively, tree-based methods can be used to better capture complex and nonlinear relationships among predictors. Suppose, more generally, J historical studies are available (J can be 1). The Alliance has been awarded a grant by the National Cancer Institute (NCI) as a member of the NCI National Clinical Trials Network (NCTN). This requires novel approaches in clinical trial design by academia and industry, and development of new assessment tools by regulatory authorities. It has been shown that as long as their values are selected to match clinical preference, mTPI’s performance is robust. With a continuous response variable, the regression model has the form: The regression coefficient η reflects subgroup effects, and testing “whether there is a subgroup effect” is equivalent to testing “whether η = 0“. However, it is a great opportunity to revisit Bayesian adaptive designs that use accumulated data and posterior probabilities for interim and final decision making for clinical trials. Usually, θT is between 1/6 to 1/3. 1989), as the origin of modern dose-finding designs, has been the most-popular choice in clinical practice for the past 30 years. Bayesian adaptive designs and methods use posterior distributions and Bayesian inference to make adaptive decisions that may alter the course of a clinical trial. One central issue is the integrity of statistical inference in maintaining the Type I error rate. Subgroup analysis is about inferring patient subpopulations with distinct treatment effects and typically focuses on identifying a subgroup that benefits more from the investigational treatment compared to the overall population. The mTPI design. Phase I trials usually involve a small number of participants (approx. This will help reduce the approval of potentially ineffective drugs that have failed in multiple prior trials but with positive results in a new confirmatory trial (e.g., the recent Alzheimer’s disease trials). Historical data are often available for the control arm, which can be obtained from previous clinical studies or routinely collected healthcare data such as electronic health records. The mTPI design and a later variation called the mTPI-2 design have been widely applied in oncology Phase I trials (e.g., the KEYNOTE-029 trial), due to their simplicity and good performance. In summary, Bayesian interim analyses can be conducted “on demand” based on thresholding the posterior probability of the drug being effective without inflating the probability of making a wrong positive decision. Suppose d is the current dose level. The mTPI design uses simple Bayesian hierarchical models to achieve two goals: (1) maintaining desirable frequentist operating characteristics of the design, and (2) providing a user-friendly implementation for clinicians. Advances in cancer treatment have provided longer survival outcomes, particularly in … At Shenandoah Oncology, clinical trials are an integral part of our commitment to providing comprehensive cancer care to our patients in Virginia, West Virginia and Maryland. A recent successful case is the study of larotrectinib in different NTRK fusion-positive tumor types (see Figure 4(a)). Certificate Program in Oncology Clinical Trials (CPOCT) This online & self-paced certificate course will provide you comprehensive training to coordinate, manage, and monitor oncology clinical trials in compliance with ICH-GCP and applicable regulatory guidelines. Otherwise, it is impossible to make a Type I error for that trial and that decision. Figure 1 illustrates the dose-finding study of dalotuzumab (10 mg kg-1) used in combination with escalating doses of MK-2206 (90–200 mg) weekly, which was guided by the mTPI design (Brana, et al. Type I error refers to a false-positive decision that rejects the null hypothesis, which typically leads to decisions that expose the drug to larger patient populations (e.g., market approval). 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