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Pediatric patients should have access to medicines that have been appropriately evaluated for safety and efficacy. Given this goal of revised labelling, the adequacy of the pediatric clinical development plan and resulting safety database must inform a favorable benefit-risk assessment for the intended use of the medicinal product. While extrapolation from adults can be used to support efficacy of drugs in children, there may be a reluctance to use the same approach in safety assessments, wiping out potential gains in trial efficiency through a reduction of sample size. To address this reluctance, we explore safety review in pediatric trials, including factors affecting these data, specific types of safety assessments, and precision on the estimation of event rates for specific adverse events (AEs) that can be achieved. In addition, we discuss the assessments which can provide a benchmark for the use of extrapolation of safety that focuses on on-target effects. Finally, we explore a unified approach for understanding precision using Bayesian approaches as the most appropriate methodology to describe/ascertain risk in probabilistic terms for the estimate of the event rate of specific AEs.

A fundamental problem in the regulatory evaluation of a therapy is assessing whether the benefit outweighs the associated risks. This work proposes designing a trial that assesses a composite endpoint consisting of benefit and risk, hence, making the core of the design of the study, to assess benefit and risk. The proposed benefit risk measure consists of efficacy measure(s) and a risk measure that is based on a composite score obtained from pre-defined adverse events of interest (AEI). This composite score incorporates full aspects of adverse events of interest (i.e. the incidence, severity, and duration of the events). We call this newly proposed score the AEI composite score. After specifying the priorities between the components of the composite endpoint, a win-statistic (i.e. win ratio, win odds, or net benefit) is used to assess the difference between treatments in this composite endpoint. The power and sample size requirements of such a trial design are explored via simulation. Finally, using Dupixent published adult study results, we show how we can design a paediatric trial where the primary outcome is a composite of prioritized outcomes consisting of efficacy endpoints and the AEI composite score endpoint. The resulting trial design can potentially substantially reduce sample size compared to a trial designed to assess the co-primary efficacy endpoints, therefore it may address the challenge of slow enrollment and patient availability for paediatric studies.

Generalized pairwise comparisons and win statistics (i.e., win ratio, win odds and net benefit) are advantageous in analyzing and interpreting a composite of multiple outcomes in clinical trials. An important limitation of these statistics is their inability to adjust for covariates other than by stratified analysis. Because the win ratio does not account for ties, the win odds, a modification that includes ties, has attracted attention. We review and combine information on the win odds to articulate the statistical inferences for the win odds. We also show alternative variance estimators based on the exact permutation and bootstrap as well as statistical inference via the probabilistic index. Finally, we extend multiple-covariate regression probabilistic index models to the win odds with a univariate outcome. As an illustration we apply the regression models to the data in the CHARM trial.

There is an increasing interest in the use of win ratio with composite time-to-event due to its flexibility in combining component endpoints. Exploring this flexibility further, one interesting question is in assessing the impact when there is a difference in treatment effect in the component endpoints. For example, the active treatment may prolong the time to occurrence of the negative event such as death or ventilation; meanwhile, the treatment effect may also shorten the time to achieving positive events, such as recovery or improvement. Notably, this portrays a situation where the treatment effect on time to recovery is in a different direction of benefit compared to the time to ventilation or death. Under such circumstances, if a single endpoint is used, the benefit gained for other individual outcomes is not counted and is diminished. As consequence, the study may need a larger sample size to detect a significant effect of treatment. Such a scenario can be handled by win ratio in a novel way by ranking component events, which is different from the usual composite endpoint approach such as time-to-first event. To evaluate how the different directions of treatment effect on component endpoints will impact the win ratio analysis, we use a Clayton copula-based bivariate survival simulation to investigate the correlation of component time-to-event. Through simulation, we found that compared to the marginal model using single endpoints, the win ratio analysis on composite endpoint performs better, especially when the correlation between two events is weak. Then, we applied the methodology to an infectious disease progression simulated study motivated by COVID-19. The application demonstrates that the win ratio approach offers advantages in empirical power compared to the traditional Cox proportional hazard approach when there is a difference in treatment effect in the marginal events.

Real-world data (RWD) can contextualize findings from single-arm trials when randomized comparative trials are unethical or unfeasible. Findings from single-arm trials alone are difficult to interpret and a comparison, when feasible and meaningful, to patient-level information from RWD facilitates the evaluation. As such, there have been several recent regulatory applications including RWD or other external data to support the product's efficacy and safety. This paper summarizes some lessons learned from such contextualization from 20 notable new drug or biologic licensing applications in oncology and rare diseases.This review focuses on 20 notable new drug or biologic licensing applications that included patient-level RWD or other external data for contextualization of trial results. Publicly available regulatory documents including clinical and statistical reviews, advisory committee briefing materials and minutes, and approved product labeling were retrieved for each application. The authors conducted independent assessments of these documents focusing on the regulatory evaluation, in each case. Three examples are presented in detail to illustrate the salient issues and themes identified across applications.Regulatory decisions were strongly influenced by the quality and usability of the RWD. Comparability of cohort attributes such as endpoints, populations, follow-up, index and censoring criteria, as well as data completeness and accuracy of key variables appeared to be essential to ensure the quality and relevance of the RWD. Given adequate sample size of the clinical trials or external control, the use of appropriate analytic methods to properly account for confounding, such as regression or matching, and pre-specification of these methods while blinded to patient outcomes seemed good strategies to address baseline differences.Contextualizing single-arm trials with patient-level RWD appears to be an advance in regulatory science; however, challenges remain. Statisticians and epidemiologists have long focused on analytical methods for comparative effectiveness but hurdles in use of RWD have often occurred upstream of the analyses. More specifically, we noted hurdles in evaluating data quality, justifying cohort selection or initiation of follow-up, and demonstrating comparability of cohorts and endpoints.

In pediatric or orphan diseases, there are many instances where it is unfeasible to conduct randomized and controlled clinical trials. This is due in part to the difficulty of enrolling a sufficient number of patients over a reasonable time period to meet adequate statistical power to demonstrate the treatment efficacy. One solution to reduce the sample size or expedite the trial timeline is to complement the current trial with real-world data. To this end, several propensity score-based methods have been developed to create defined groups of patients that are controlled for confounding based on a set of measured covariates at baseline. However, balance checking on the measured covariates and tweaks to the propensity score models is usually inevitable to achieve the joint balance across all covariates. To mitigate this iterative procedure, we utilize the entropy balancing weighting technique which focuses on balancing the covariates of subjects between the experimental and control groups directly and augments the current trial with the external control data via a power prior. The finite-sample properties of the proposed method are assessed via simulations in the context of asymmetrically randomized controlled trials where only a small portion of patients are randomized to the control group. Other methods such as covariate-balancing propensity score (CBPS) and propensity score matching (PSM) and weighting (PSW) are also compared to provide context on the operating characteristics of the proposed method.

Pediatric drug development has many unique challenges, one of which is the evaluation of growth and development changes in children that are expected and are not due to the study intervention. Children grow and mature at different pace. The potential impact of the drug could vary with the developmental age of the participants receiving the treatment. For example, sexual maturation is a critical consideration in children of age 10 and above, but not in younger age groups. How the investigational drug impacts children is ultimately a risk-benefit consideration. In this paper, practical considerations and recommendations are provided on how to assess growth and development based on data collected from clinical trials in pediatric patients. The endpoints and measures related to growth, sexual maturation and neurocognitive development are discussed. Basic analysis approaches are recommended.

The use of Bayesian methodology to design and analyze pediatric efficacy trials is one of the possible options to reduce their sample size. This reduction of the sample size results from the use of an informative prior for the parameters of interest. In most of the applications, the principle of 'information borrowing' from adults' trials is applied, which means that the informative prior is constructed using efficacy results in adult of the drug under investigation. This implicitly assumes similarity in efficacy between the selected pediatric dose and the efficacious dose in adults. The goal of this article is to propose a method to construct prior distribution for the parameter of interest, not directly constructed from the efficacy results of the efficacious dose in adult patients but using pharmacodynamic modeling of a bridging biomarker using early phase pediatric data. When combined with a model bridging the biomarker with the clinical endpoints, the prior is constructed using a variational method after simulation of the parameters of interest. A use case application illustrates how the method can be used to construct a realistic informative prior.

In many orphan diseases and pediatric indications, the randomized controlled trials may be infeasible because of their size, duration, and cost. Leveraging information on the control through a prior can potentially reduce sample size. However, unless an objective prior is used to impose complete ignorance for the parameter being estimated, it results in biased estimates and inflated type-I error. Hence, it is essential to assess both the confirmatory and supplementary knowledge available during the construction of the prior to avoid "cherry-picking" advantageous information. For this purpose, propensity score methods are employed to minimize selection bias by weighting supplemental control subjects according to their similarity in terms of pretreatment characteristics to the subjects in the current trial. The latter can be operationalized through a proposed measure of overlap in propensity-score distributions. In this paper, we consider single experimental arm in the current trial and the control arm is completely borrowed from the supplemental data. The simulation experiments show that the proposed method reduces prior and data conflict and improves the precision of the of the average treatment effect.

Estimands play an important role for aligning study objectives, study design and analyses through a precise definition of the quantity of interest. For COVID-19 studies, apart from intercurrent events, high volume of missing data has been observed. We explore their impact on several estimands through a synthetic COVID-19 data generated from a discrete-time multi-state model. We compare estimators of these estimands based on their ability to closely match the true response rates and retain assumed power. The final choice of the estimand then needs to be aligned with clinically meaningful quantities of interest to patients, clinicians, regulators and payers.

As the regulatory environment becomes progressively receptive toward utilizing real-world evidence, a spectrum of real-world data incorporation techniques in trial conduct and analysis has seen increasing interest and adoption in different stages of drug development. Of particular interest is leveraging external control data to augment the control arm in a concurrent randomized controlled trial, where patients are enrolled in both investigational treatment arm and the control arm. Yet despite the emerging literature in external data borrowing in a hybrid trial setting, very little discussion focuses on delineating what should be matched and what is actually being estimated, especially when a variety of matching schemes can be considered. In general, external control can be matched in four different ways: (1) matching with the intersection between investigational treatment and concurrent control, (2) matching with the union of concurrent investigational treatment and concurrent control, (3) matching with concurrent control alone, and (4) matching with investigational treatment alone. In this article, the formulation of estimands for different matching schemes are detailed to describe what these matching methods facilitate to answer. Simulation studies are also conducted to evaluate the performance characteristics under different matching schemes, estimation methods, effect size assumptions, and missingness of confounders.

For the clinical studies in rare diseases or small patient populations, having an adequately powered randomized controlled trial is further complicated by variability. As such, sample size re-estimation can be a useful tool if at an interim look the trial sample size needs to be increased to achieve adequate power to reject the null hypothesis. Meanwhile, borrowing or extrapolating information from real-world data or real-world evidence has gained increasing use in trial design and analysis since 2014. Combining these two strategies, high-quality real-world data, if leveraged properly, has the potential to generate real-world evidence that can assist interim decision-making, lower enrollment burden, and reduce study timeline and costs. With proper borrowing from historical control, some of the challenges in these high unmet medical need studies could be resolved considerably. We examine the incorporation of real-world evidence within the framework of adaptive design strategy in pediatric type II diabetes trials where recruitment has been challenging and the completion is hardly on time. Simulations under various scenarios are conducted to assess the borrowing strategy, i.e., the matching method in combination of sample size re-estimation. Comparisons of performance metrics are presented to showcase the advantages of proposed method.

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Conventional analyses of a composite of multiple time-to-event outcomes use the time to the first event. However, the first event may not be the most important outcome. To address this limitation, generalized pairwise comparisons and win statistics (win ratio, win odds, and net benefit) have become popular and have been applied to clinical trial practice. However, win ratio, win odds, and net benefit have typically been used separately. In this article, we examine the use of these three win statistics jointly for time-to-event outcomes. First, we explain the relation of point estimates and variances among the three win statistics, and the relation between the net benefit and the Mann-Whitney U statistic. Then we explain that the three win statistics are based on the same win proportions, and they test the same null hypothesis of equal win probabilities in two groups. We show theoretically that the Z-values of the corresponding statistical tests are approximately equal; therefore, the three win statistics provide very similar p-values and statistical powers. Finally, using simulation studies and data from a clinical trial, we demonstrate that, when there is no (or little) censoring, the three win statistics can complement one another to show the strength of the treatment effect. However, when the amount of censoring is not small, and without adjustment for censoring, the win odds and the net benefit may have an advantage for interpreting the treatment effect; with adjustment (e.g., IPCW adjustment) for censoring, the three win statistics can complement one another to show the strength of the treatment effect. For calculations we use the R package WINS, available on the CRAN (Comprehensive R Archive Network).

Among many efforts to facilitate timely access to safe and effective medicines to children, increased attention has been given to extrapolation. Loosely, it is the leveraging of conclusions or available data from adults or older age groups to draw conclusions for the target pediatric population when it can be assumed that the course of the disease and the expected response to a medicinal product would be sufficiently similar in the pediatric and the reference population. Extrapolation then can be characterized as a statistical mapping of information from the reference (adults or older age groups) to the target pediatric population. The translation, or loosely mapping of information, can be through a composite likelihood approach where the likelihood of the reference population is weighted by exponentiation and that this exponent is related to the value of the mapped information in the target population. The weight is bounded above and below recognizing the fact that similarity (of the disease and the expected response) is still valid despite variability of response between the cohorts. Maximum likelihood approaches are then used for estimation of parameters and asymptotic theory is used to derive distributions of estimates for use in inference. Hence, the estimation of effects in the target population borrows information from reference population. In addition, this manuscript also talks about how this method is related to the Bayesian statistical paradigm., Comment: 28 pages, 4 figures, 3 tables

Pediatric drug development lags adult development by about 8 years (Mulugeta et al. in Pediatr Clin 64(6):1185-1196, 2017). In such context, many incentives, regulations, and innovative techniques have been proposed to address the disparity for pediatric patients. One such strategy is extrapolation of efficacy from a reference population. Extrapolation is currently justified by providing evidence in support of the effective use of drugs in children when the course of the disease and the expected treatment response would be sufficiently similar in the pediatric and reference population. This paper's position is that, despite uncertainties, pediatric drug development programs should initially assume some degree of extrapolation. The degree to which extrapolation can be used lies along a continuum representing the uncertainties to be addressed through generation of new pediatric evidence. In addressing these uncertainties, the extrapolation strategy should reflect the level of tolerable uncertainty concerning the decision to expose a child to the risks of a new drug. This judgment about the level of tolerable uncertainty should vary with the context (e.g., disease severity, existing therapeutic options) and can be embedded into pediatric drug development archetypes to ascertain the extent of studies needed and whether simultaneous development for adults and adolescents be considered.

The conduct of pediatric clinical trials is legally required, monitored, and encouraged in major geographic areas such as the United States and Europe. However, because pediatric patients are considered vulnerable populations, they should only be enrolled as research subjects in a clinical trial if enrolling adult subjects will not be able to answer the scientific question related to the health and welfare of children. Thus, there is an ethical obligation to build the foundation for the use of pediatric extrapolation and related innovative analytical strategies with appropriately designed pediatric and adult clinical trials to reduce the amount of, or general need for, additional information needed from children to reach conclusions. This manuscript discusses innovative applications of clinical trial designs, analytic strategies to more efficiently leverage prior information, and modeling approaches that impact the data required to determine efficacy of an investigational drug in pediatrics. The planning of pediatric trials and regulatory interactions related to required pediatric studies and the expectations for innovative analytics are also discussed.

Determining whether there are differential treatment effects in subgroups of trial participants remains an important topic in clinical trials as precision medicine becomes ever more relevant. Any assessment of differential treatment effect is predicated on being able to estimate the treatment response accurately while satisfying constraints of balancing the risk of overlooking an important subgroup with the potential to make a decision based on a false discovery. While regression models, such as marginal interaction model, have been widely used to improve accuracy of subgroup parameter estimates by leveraging the relationship between treatment and covariate, there is still a possibility that it can lead to excessively conservative or anti-conservative results. Conceivably, this can be due to the use of the normal distribution as a default prior, which forces outlying subjects to have their means over-shrunk towards the population mean, and the data from such subjects may be excessively influential in estimation of both the overall mean response and the mean response for each subgroup, or a model mis-specification. To address this issue, we investigate the use of nonparametric Bayes, particularly Dirichlet process priors, to create semi-parametric models. These models represent uncertainty in the prior distribution for the overall response while accommodating heterogeneity among individual subgroups. They also account for the effect and variation due to the unaccounted terms. As a result, the models do not force estimates to excessively shrink but still retain the attractiveness of improved precision given by the narrower credible intervals. This is illustrated in extensive simulations investigating bias, mean squared error, coverage probability and credible interval widths. We applied the method on a simulated data based closely on the results of a cystic fibrosis Phase 2 trial.

The win ratio method has received much attention in methodological research, ad hoc analyses, and designs of prospective studies. As the primary analysis, it supported the approval of tafamidis for the treatment of cardiomyopathy to reduce cardiovascular mortality and cardiovascular-related hospitalization. However, its dependence on censoring is a potential shortcoming. In this article, we propose the inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic (i.e., the IPCW-adjusted win ratio statistic) to overcome censoring issues. We consider independent censoring, common censoring across endpoints, and right censoring. We develop an asymptotic variance estimator for the logarithm of the IPCW-adjusted win ratio statistic and evaluate it via simulation. Our simulation studies show that, as the amount of censoring increases, the unadjusted win proportions may decrease greatly. Consequently, the bias of the unadjusted win ratio estimate may increase greatly, producing either an overestimate or an underestimate. We demonstrate theoretically and through simulation that the IPCW-adjusted win ratio statistic gives an unbiased estimate of treatment effect.

The US Food and Drug Administration (FDA) has shown scientific discretion in interpreting the substantial evidence requirement for the approval of new drugs with its considerations on the use of single controlled or uncontrolled trials (Federal Food, Drug, and Cosmetic Act § 505(d), 21 USC 355(d), 1962). With the passage of the 21st Centuries Cures Act (21st Century Cures-patients. House, Energy and Commerce Committee, Washington, DC, 2019 available at: https://energycommerce.house.gov/sites/republicans.energycommerce.house.gov/files/analysis/21stCenturyCures/20140516PatientsWhitePaper.pdf ), the FDA is mandated to expand the role of real-world evidence (RWE) in support of drug approval. This mandate further broadens the scope of scientific discretion to include data collected outside clinical trials. We summarize the agency's past acceptance of real-world data (RWD) sources for supporting drug approval in new indications which have been reflected in US labels. In our summary, we focus on the type of RWD and statistical methodologies presented in these labels. Furthermore, two labels were selected for in-depth assessment of the RWE presented in these labels. Through these examples, we demonstrate the issues that can be raised in data collection that could affect interpretation. In addition, a brief discussion of statistical methods that can be used to incorporate RWE to clinical development is presented.

Children represent a large underserved population of "therapeutic orphans," as an estimated 80% of children are treated off-label. However, pediatric drug development often faces substantial challenges, including economic, logistical, technical, and ethical barriers, among others. Among many efforts trying to remove these barriers, increased recent attention has been paid to extrapolation; that is, the leveraging of available data from adults or older age groups to draw conclusions for the pediatric population. The Bayesian statistical paradigm is natural in this setting, as it permits the combining (or "borrowing") of information across disparate sources, such as the adult and pediatric data. In this paper, authored by the pediatric subteam of the Drug Information Association Bayesian Scientific Working Group and Adaptive Design Working Group, we develop, illustrate, and provide suggestions on Bayesian statistical methods that could be used to design improved pediatric development programs that use all available information in the most efficient manner. A variety of relevant Bayesian approaches are described, several of which are illustrated through 2 case studies: extrapolating adult efficacy data to expand the labeling for Remicade to include pediatric ulcerative colitis and extrapolating adult exposure-response information for antiepileptic drugs to pediatrics.

Extrapolation from adults to youth with type 2 diabetes (T2D) is challenged by differences in disease progression and manifestation. This manuscript presents the results of a mock-team workshop focused on examining the typical team-based decision process used to propose a pediatric development plan for T2D addressing the viability of extrapolation. The workshop was held at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) in Orlando, Florida on March 21, 2018.

Pediatric clinical trials are often requested according to specific age ranges. In the past and still today, these ages may correspond to developmental stages, such as newborn, infancy, childhood, and adolescence. Selection of ages for pediatric participation in medication studies should correspond to ages of rapid changes in pharmacokinetics and pharmacodynamics. Age-related changes in several enzymes involved in drug metabolism and glomerular filtration are described as examples of optimal ages for study of specific drugs according to their pathways of disposition.

In the absence of placebo-controlled trials, the efficacy of a test treatment can be alternatively examined by showing its non-inferiority to an active control; that is, the test treatment is not worse than the active control by a pre-specified margin. The margin is based on the effect of the active control over placebo in historical studies. In other words, the non-inferiority setup involves a network of direct and indirect comparisons between test treatment, active controls, and placebo. Given this framework, we consider a Bayesian network meta-analysis that models the uncertainty and heterogeneity of the historical trials into the non-inferiority trial in a data-driven manner through the use of the Dirichlet process and power priors. Depending on whether placebo was present in the historical trials, two cases of non-inferiority testing are discussed that are analogs of the synthesis and fixed-margin approach. In each of these cases, the model provides a more reliable estimate of the control given its effect in other trials in the network, and, in the case where placebo was only present in the historical trials, the model can predict the effect of the test treatment over placebo as if placebo had been present in the non-inferiority trial. It can further answer other questions of interest, such as comparative effectiveness of the test treatment among its comparators. More importantly, the model provides an opportunity for disproportionate randomization or the use of small sample sizes by allowing borrowing of information from a network of trials to draw explicit conclusions on non-inferiority.

Existing statutes in the United States and Europe require manufacturers to demonstrate evidence of effectiveness through the conduct of adequate and well-controlled studies to obtain marketing approval of a therapeutic product. What constitutes adequate and well-controlled studies is usually interpreted as randomized controlled trials (RCTs). However, these trials are sometimes unfeasible because of their size, duration, cost, patient preference, or in some cases, ethical concerns. For example, RCTs may not be fully powered in rare diseases or in infections caused by multidrug resistant pathogens because of the low number of enrollable patients. In this case, data available from external controls (including historical controls and observational studies or data registries) can complement information provided by RCT. Propensity score matching methods can be used to select or "borrow" additional patients from the external controls, for maintaining a one-to-one randomization between the treatment arm and active control, by matching the new treatment and control units based on a set of measured covariates, ie, model-based pairing of treatment and control units that are similar in terms of their observable pretreatment characteristics. To this end, 2 matching schemes based on propensity scores are explored and applied to a real clinical data example with the objective of using historical or external observations to augment data in a trial where the randomization is disproportionate or asymmetric.

Drug developers are required to demonstrate substantial evidence of effectiveness through the conduct of adequate and well-controlled (A&WC) studies to obtain marketing approval of their medicine. What constitutes A&WC is interpreted as the conduct of randomized controlled trials (RCTs). However, these trials are sometimes unfeasible because of their size, duration, and cost. One way to reduce sample size is to leverage information on the control through a prior. One consideration when forming data-driven prior is the consistency of the external and the current data. It is essential to make this process less susceptible to choosing information that only helps improve the chances toward making an effectiveness claim. For this purpose, propensity score methods are employed for two reasons: (1) it gives the probability of a patient to be in the trial, and (2) it minimizes selection bias by pairing together treatment and control within the trial and control subjects in the external data that are similar in terms of their pretreatment characteristics. Two matching schemes based on propensity scores, estimated through generalized boosted methods, are applied to a real example with the objective of using external data to perform Bayesian augmented control in a trial where the allocation is disproportionate. The simulation results show that the data augmentation process prevents prior and data conflict and improves the precision of the estimator of the average treatment effect.

The paradigm shift towards precision medicine reignited interest in determining whether there are differential treatment effects in subgroups of trial participants. Intrinsic to this problem is that any assessment of a differential treatment effect is predicated on being able to estimate the treatment response accurately while satisfying constraints of balancing the risk of overlooking an important subgroup with the potential to make a decision based on a false discovery. While shrinkage models have been widely used to improve accuracy of subgroup parameter estimates by leveraging the relationship between them, there is still a possibility that it can lead to excessively conservative or anti-conservative results. This can possibly be due to the use of the normal distribution as prior, which forces outlying subjects to have their means over-shrunk towards the population mean, and the data from such subjects may be excessively influential in estimation of both the overall mean response and the mean response for each subgroup, or a model misspecification due to unaccounted variation or clustering. To address this issue, we investigate the use of nonparametric Bayes, particularly Dirichlet process priors, to create a flexible shrinkage model. This model represents uncertainty in the prior distribution for the overall response while accommodating heterogeneity among individual subgroups. We simulated data to compare estimates when there is no differential subgroup effect and when there is a differential subgroup effect. In either of these scenarios, the flexible shrinkage model does not force estimates to shrink excessively when similarity of treatment effects is not supported but still retains the attractiveness of improved precision given by the narrower credible intervals. We also applied the same method to a dataset based on trials conducted for an antimicrobial therapy on several related indications.

The gold standard for evaluating treatment efficacy of a medical product is a placebo-controlled trial. However, when the use of placebo is considered to be unethical or impractical, a viable alternative for evaluating treatment efficacy is through a noninferiority (NI) study where a test treatment is compared to an active control treatment. The minimal objective of such a study is to determine whether the test treatment is superior to placebo. An assumption is made that if the active control treatment remains efficacious, as was observed when it was compared against placebo, then a test treatment that has comparable efficacy with the active control, within a certain range, must also be superior to placebo. Because of this assumption, the design, implementation, and analysis of NI trials present challenges for sponsors and regulators. In designing and analyzing NI trials, substantial historical data are often required on the active control treatment and placebo. Bayesian approaches provide a natural framework for synthesizing the historical data in the form of prior distributions that can effectively be used in design and analysis of a NI clinical trial. Despite a flurry of recent research activities in the area of Bayesian approaches in medical product development, there are still substantial gaps in recognition and acceptance of Bayesian approaches in NI trial design and analysis. The Bayesian Scientific Working Group of the Drug Information Association provides a coordinated effort to target the education and implementation issues on Bayesian approaches for NI trials. In this article, we provide a review of both frequentist and Bayesian approaches in NI trials, and elaborate on the implementation for two common Bayesian methods including hierarchical prior method and meta-analytic-predictive approach. Simulations are conducted to investigate the properties of the Bayesian methods, and some real clinical trial examples are presented for illustration.