Design and Evaluation of Multiregional Trials with Heterogeneous Treatment Effect Across Regions

Chen, Chi-Tian; Hung, H. M. James; Hsiao, Chin‐Fu

doi:10.1080/10543406.2012.701585

Cited by 13 publications

(12 citation statements)

References 14 publications

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“…In addition to the fixed-effect model, the random-effect model has been studied for MRCTs (Chen et al, 2012b;Hung et al, 2010). However, only the fixed-effect model is considered in this paper.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Statistical implications of extrapolating the overall result to the target region in multi-regional clinical trials

Kang

Kim

2018

CSAM

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The one of the principles described in ICH E9 is that only results obtained from pre-specified statistical methods in a protocol are regarded as confirmatory evidence. However, in multi-regional clinical trials, even when results obtained from pre-specified statistical methods in protocol are significant, it does not guarantee that the test treatment is approved by regional regulatory agencies. In other words, there is no so-called global approval, and each regional regulatory agency makes its own decision in the face of the same set of data from a multi-regional clinical trial. Under this situation, there are two natural methods a regional regulatory agency can use to estimate the treatment effect in a particular region. The first method is to use the overall treatment estimate, which is to extrapolate the overall result to the region of interest. The second method is to use regional treatment estimate. If the treatment effect is completely identical across all regions, it is obvious that the overall treatment estimator is more efficient than the regional treatment estimator. However, it is not possible to confirm statistically that the treatment effect is completely identical in all regions. Furthermore, some magnitude of regional differences within the range of clinical relevance may naturally exist for various reasons due to, for instance, intrinsic and extrinsic factors. Nevertheless, if the magnitude of regional differences is relatively small, a conventional method to estimate the treatment effect in the region of interest is to extrapolate the overall result to that region. The purpose of this paper is to investigate the effects produced by this type of extrapolation via estimations, followed by hypothesis testing of the treatment effect in the region of interest. This paper is written from the viewpoint of regional regulatory agencies.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Statistical implications of extrapolating the overall result to the target region in multi-regional clinical trials

Kang

Kim

2018

CSAM

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“…Quan et al., 13 Tsong et al., 30 and Chen et al. 31 discuss hypothesis testing methods and properties for consistency assessment based on {δg;g=1,…,G} from frequentist perspective.…”

Section: Methodsmentioning

confidence: 99%

Hierarchical Bayes approach for subgroup analysis

Hsu

Zalkikar

Tiwari

2017

Stat Methods Med Res

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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.

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“…In addition, consistency criteria different from those of the Japan guidance have been established, such as those by Tsou et al [6], Uesaka [7], Ko et al [8], and Tsou et al [9]. On the other hand, Chen et al [10] and Huang et al [11] considered ethnic differences and proposed methods that apply different treatment effects across regions to the design and evaluation of MRCTs.…”

Section: Introductionmentioning

confidence: 99%

Sample size determination for a specific region in multiregional clinical trials with multiple co-primary endpoints

et al. 2017

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Recently, multi-regional clinical trials (MRCTs), which incorporate subjects from many countries/regions around the world under the same protocol, have been widely conducted by many global pharmaceutical companies. The objective of such trials is to accelerate the development process for a drug and shorten the drug’s approval time in key markets. Several statistical methods have been purposed for the design and evaluation of MRCTs, as well as for assessing the consistency of treatment effects across all regions with one primary endpoint. However, in some therapeutic areas (e.g., Alzheimer’s disease), the clinical efficacy of a new treatment may be characterized by a set of possibly correlated endpoints, known as multiple co-primary endpoints. In this paper, we focus on a specific region and establish three statistical criteria for evaluating consistency between the specific region and overall results in MRCTs with multiple co-primary endpoints. More specifically, two of those criteria are used to assess whether the treatment effect in the region of interest is as large as that of the other regions or of the regions overall, while the other criterion is used to assess the consistency of the treatment effect of the specific region achieving a pre-specified threshold. The sample size required for the region of interest can also be evaluated based on these three criteria.

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Design and Evaluation of Multiregional Trials with Heterogeneous Treatment Effect Across Regions

Cited by 13 publications

References 14 publications

Statistical implications of extrapolating the overall result to the target region in multi-regional clinical trials

Statistical implications of extrapolating the overall result to the target region in multi-regional clinical trials

Hierarchical Bayes approach for subgroup analysis

Sample size determination for a specific region in multiregional clinical trials with multiple co-primary endpoints

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