Assessment of Regional Treatment Effect in a Multiregional Clinical Trial

Bayesian approach for design and analysis of medical device trials in the era of modern clinical studies

Medical device technology develops rapidly, and the life cycle of a medical device is much shorter than drugs. It is necessary to evaluate the safety and effectiveness of a medical device in a timely manner to keep up with technology flux. Bayesian methods provides an efficient approach to addressing this challenge. In this article, we review the characteristics of the Bayesian approach and some Bayesian designs that were commonly used in medical device regulatory setting, including Bayesian adaptive design, Bayesian diagnostic design, Bayesian multiregional design, and Bayesian label expansion study. We illustrate these designs with medical devices approved by the US Food and Drug Administration (FDA). We also review several innovative Bayesian information borrowing methods, and briefly discuss the challenges and future directions of the Bayesian application in medical device trials. Our objective is to promote the use of the Bayesian approach to accelerate the development of innovative medical devices and their accessibility to patients for effective disease diagnoses and treatments.

Bayesian multiregional clinical trials using model averaging

Multiregional clinical trials (MRCTs) provide the benefit of more rapidly introducing drugs to the global market; however, small regional sample sizes can lead to poor estimation quality of region-specific effects when using current statistical methods. With the publication of the International Conference for Harmonisation E17 guideline in 2017, the MRCT design is recognized as a viable strategy that can be accepted by regional regulatory authorities, necessitating new statistical methods that improve the quality of region-specific inference. In this article, we develop a novel methodology for estimating region-specific and global treatment effects for MRCTs using Bayesian model averaging. This approach can be used for trials that compare two treatment groups with respect to a continuous outcome, and it allows for the incorporation of patient characteristics through the inclusion of covariates. We propose an approach that uses posterior model probabilities to quantify evidence in favor of consistency of treatment effects across all regions, and this metric can be used by regulatory authorities for drug approval. We show through simulations that the proposed modeling approach results in lower MSE than a fixed-effects linear regression model and better control of type I error rates than a Bayesian hierarchical model.

Regional consistency and sample size considerations in a multiregional equivalence trial

The main objective of a confirmatory multiregional clinical trial (MRCT) is to demonstrate the overall efficacy of test drugs in all participating regions as well as to evaluate the possibility of extrapolating the overall results to each region. With the emergence of the demands of biosimilar drugs development, some guidelines recommended using equivalence design to demonstrate the comparability of efficacy between biosimilar and reference drugs. Previous discussions about assessing regional consistency in MRCT are mainly focused on superiority or non-inferiority designs, while the extensions to equivalence designs were limited. In this work, we proposed a flexible regional consistency criterion for the MRCT with equivalence design. Based on this criterion, sample size determination and sample allocation were also discussed.

Semi-parametric Bayesian regression for subgroup analysis in clinical trials

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.

Clinical Development and Initial Approval of Novel Immune Checkpoint Inhibitors in Oncology: Insights From a Global Regulatory Perspective

Immune checkpoint inhibitors (ICI) have demonstrated meaningful patterns of clinical efficacy across various cancers. During their development, novel regulatory strategies and clinical design approaches were explored. This metrics-based narrative review examines submission strategies and clinical evidence expectations of the US, European, and Japanese drug agencies, as well as their impact on approval and overall development times. Also discussed is the role of emerging clinical science and biomarker evaluation to get the first six ICI initially approved.

Hierarchical Bayes approach for subgroup analysis

In clinical data analysis, both treatment effect estimation and consistency assessment are important for a better understanding of the drug efficacy for the benefit of subjects in individual subgroups. The linear mixed-effects model has been used for subgroup analysis to describe treatment differences among subgroups with great flexibility. The hierarchical Bayes approach has been applied to linear mixed-effects model to derive the posterior distributions of overall and subgroup treatment effects. In this article, we discuss the prior selection for variance components in hierarchical Bayes, estimation and decision making of the overall treatment effect, as well as consistency assessment of the treatment effects across the subgroups based on the posterior predictive p-value. Decision procedures are suggested using either the posterior probability or the Bayes factor. These decision procedures and their properties are illustrated using a simulated example with normally distributed response and repeated measurements.

A nested group sequential framework for regional evaluation in global drug development program

The primary objective of a multiregional clinical trial (MRCT) is to assess the efficacy of all participating regions and evaluate the probability of applying the overall results to a specific region. The consistency assessment of the target region with the overall results is the most common way of evaluating the efficacy in a specific region. Recently, Huang et al. (2012) proposed an additional trial in the target region to an MRCT to evaluate the efficacy in the target ethnic (TE) population under the framework of simultaneous global drug development program (SGDDP). However, the operating characteristics of this statistical framework were not well considered. Therefore, a nested group sequential program for regional efficacy evaluation is proposed in this paper. It is an extension of Huang's SGDDP framework and allows interim analysis after MRCT and in the course of local clinical trial (LCT) phase. It is able to well control the family-wise type I error in the program level and enhances the flexibility of the program. In LCT sample size estimation, we introduce virtual trial, which is transformed from the original program by using discounting factor, and an iteration method is employed to calculate the sample size and stopping boundaries of interim analyses. The proposed sample size estimation method is validated in the simulations and the effect of varied weight, effect size of TE population, and design setting is explored. Examples with normal end point, binary end point, and survival end point are shown to illustrate the application of the proposed nested group sequential program.

Unified additional requirement in consideration of regional approval for multiregional clinical trials

To speed up the process of bringing a new drug to the market, more and more clinical trials are being conducted simultaneously in multiple regions. After demonstrating the overall drug's efficacy across regions, the regulatory and drug sponsor may also want to assess the drug's effect in specific region(s). Most of the recent approaches imposed a uniform criterion to assess the consistency of treatment effects between the interested region(s) and the entire study population regardless of the number of regions in multiregional clinical trials (MRCT). As a result, the needed sample size to achieve the desired probability of satisfying the regional requirement could be huge and implausible for the trial sponsors to implement. In this paper, we propose a unified additional requirement for regional approval by differing the parameters in the additional requirement depending on the number of planned regions. In particular, the values of the parameters are determined by a reasonable sample size increase with the desired probability satisfying the additional requirement. Considering the practicality of the global trial or sample size increase, we recommend specific values of the parameters for a different number of planned regions. We also introduce the assurance probability curve to evaluate the performance of different regional requirements.

Regional efficacy assessment in multiregional clinical development

It is common in multiregional clinical development that data from a global trial and a local trial (in a target country) together will be used to support local filing in the target country. This approach is considered efficient drug development both globally and in the target country. However, it remains a challenge how to combine global trial data and local trial data toward local filing. To address this challenge, we propose an "interpretation-centric" evaluation criterion based on a weighted estimator that weights data from the target country and outside of the target country. This approach provides an unbiased estimate of a global treatment effect with appropriate representation of the target country patient population, where the "appropriate representation" is the desired proportion of the target country participants in a global trial and is measured by the weight parameter. This natural interpretation can facilitate drug development discussion with local regulatory agencies. Sample size of the local trial can be determined using the proposed weighted estimator. Approaches for weight determination are also discussed.

Extrapolation of efficacy and other data to support the development of new medicines for children: A systematic review of methods

OBJECTIVE

When developing new medicines for children, the potential to extrapolate from adult data to reduce the experimental burden in children is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. We reviewed the literature to identify statistical methods that could be used to optimise extrapolations in paediatric drug development programmes.

METHODS

Web of Science was used to identify papers proposing methods relevant for using data from a 'source population' to support inferences for a 'target population'. Four key areas of methods development were targeted: paediatric clinical trials, trials extrapolating efficacy across ethnic groups or geographic regions, the use of historical data in contemporary clinical trials and using short-term endpoints to support inferences about long-term outcomes.

RESULTS

Searches identified 626 papers of which 52 met our inclusion criteria. From these we identified 102 methods comprising 58 Bayesian and 44 frequentist approaches. Most Bayesian methods (n = 54) sought to use existing data in the source population to create an informative prior distribution for a future clinical trial. Of these, 46 allowed the source data to be down-weighted to account for potential differences between populations. Bayesian and frequentist versions of methods were found for assessing whether key parameters of source and target populations are commensurate (n = 34). Fourteen frequentist methods synthesised data from different populations using a joint model or a weighted test statistic.

CONCLUSIONS

Several methods were identified as potentially applicable to paediatric drug development. Methods which can accommodate a heterogeneous target population and which allow data from a source population to be down-weighted are preferred. Methods assessing the commensurability of parameters may be used to determine whether it is appropriate to pool data across age groups to estimate treatment effects.

Reliably basing conclusions on subgroups of randomized clinical trials

The classical paradigm of Phase III clinical research is to demonstrate efficacy of a drug in an unselected patient population representative for later clinical practice. The flip side of the coin is that homogeneity of the treatment effect in subpopulations of the patient population cannot be assumed to be trivially given. Close inspection of relevant subgroups is important, as soon as overall efficacy has been demonstrated. This may lead to restrictions regarding the patient population to be treated. Similarly, although subgroup findings may be misleading, it should be possible in rare instances to base valid conclusions on subgroups of trials where this has not been precisely prespecified. Subgroups in multiregional clinical trials are different and deserve special consideration.

Global distribution pattern of histological subtypes of epithelial ovarian cancer: a database analysis and systematic review

BACKGROUND

Epithelial ovarian cancer is basically a heterogeneous disease with different chemosensitivity and distinct molecular alternations for each histological subtype. In order to assess whether the results of clinical trials can be extrapolated to a new country, it is critical to first examine whether the relative frequencies is homogenous across countries.

METHODS

Cancer registry database from a single institution in Taiwan combined with systematic review of the global literature on the relative frequencies of histological subtypes between 2003 and 2012 was provided.

RESULTS

Of 175 titles identified, 41 studies met inclusion/exclusion criteria. Globally, for each subtype, the median value of relative frequencies for serous subtype was 45.0%, with the Philippines (16.0%), Indonesia (22.7%), and Brazil (30.1%) as the three lowest countries and South Africa (68.0%), Greece (71.5%), and India (86.7%) as the three highest countries; for mucinous subtype, 11.4%, Italy (3.0%), Australia (3.4%), and Japan (5.4%) were the three lowest countries, while Indonesia (29.1%), Singapore (30.3%), and South Korea (38.6%) were the three highest countries; for endometrioid subtype, 12.6%, India (1.6%), Greece (5.7%), and Portugal (7.6%) were the three lowest countries, while Taiwan (24.8%), Egypt (25.0%), and Austria (25.5%) were the three highest countries; and for clear cell subtype, 5.3%, Pakistan (1.0%), Iran (2.0%), and Brazil (2.1%) were the three lowest countries while Thailand (16.0%), Taiwan (16.8%), and Spain (18.8%) were the three highest countries.

CONCLUSIONS

Relative frequencies of subtypes were not homogenous across countries. This diversity may reflect the geographical and ethnic variations. Globally, epithelial ovarian cancer is a heterogeneous disease with a heterogeneous distribution pattern.