Assessing the consistency of the treatment effect under the discrete random effects model in multiregional clinical trials

Liu, Jung-Tzu; Tsou, Hsiao‐Hui; Lan, K. K. Gordon; Chen, Chi-Tian; Lai, Yi-Chun; Chang, Wan-Jung; Tzeng, Chyng‐Shyan

doi:10.1002/sim.6869

Cited by 13 publications

(8 citation statements)

References 18 publications

(28 reference statements)

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“…When estimating the overall treatment effect in multiregional trials, we only considered fixed effect in this work. However, regional heterogeneity on treatment effect can be expected and random effect model can be applied 13,17,18 …”

Section: Discussionmentioning

confidence: 99%

Regional consistency and sample size considerations in a multiregional equivalence trial

Zhang

et al. 2020

Pharmaceutical Statistics

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SUMMARY 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.

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Section: Discussionmentioning

confidence: 99%

Regional consistency and sample size considerations in a multiregional equivalence trial

Zhang

et al. 2020

Pharmaceutical Statistics

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“…Recently, many researchers have developed new methods for determining the sample size for each region. 12 -18 These methods may be useful for evaluating the heterogeneity of the effect of an investigational drug or for sample size determination for a specific local region.…”

Section: Discussionmentioning

confidence: 99%

An Analysis of Japanese Patients Enrolled in Multiregional Clinical Trials in Oncology

Hirakawa

Kinoshita

2017

Ther Innov Regul Sci

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The Japanese regulatory agency, the Ministry of Health, Labour and Welfare, requires sponsors to enroll a specific number or proportion of Japanese patients in multiregional clinical trials (MRCTs) in order to allow for the appropriate statistical evaluation of the efficacy and safety of an investigational drug in the Japanese population. This means the actual proportion of Japanese patients to the total sample size would need to be determined by taking into account the proportion of patients in other regions as well as the appropriate statistical considerations. Determining the proportion of Japanese patients that satisfies the regulatory agency's statistical requirement, along with taking into account the practical limitations of patient enrollment, would be difficult for sponsors. We believe that recent studies about the proportion of Japanese patients enrolled in MRCTs provides sponsors with useful information about partitioning sample size into individual regions for MRCTs in oncology. In this study, we investigated the proportion of Japanese patients in MRCTs and further compared the efficacy results from the overall population to that of the Japanese population. The proportion of Japanese patients averaged approximately 10.9%, but the proportion varied depending on the drug type. The results of the primary endpoints in Japanese patients were similar to those of the overall population, regardless of the proportion of Japanese patients.

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“…The DREM departs from the usual fixed or continuous random effects assumption to acknowledge that regional difference Liu et al . proposed models for consistency under DREM. This paper deals with the practice of the drop‐min approach for the treatment effect estimate.…”

Section: Discussionmentioning

confidence: 99%

“…DREM assumes that treatment effects over regions are different. Consistency under DREM have been investigated by Liu et al [9].…”

Section: Introductionmentioning

confidence: 99%

Inconsistency and drop‐minimum data analysis

Chen

Lan

2016

Statistics in Medicine

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Even though consistency is an important issue in multi-regional clinical trials and inconsistency is often anticipated, solutions for handling inconsistency are rare. If a region's treatment effects are inconsistent with that of the other regions, pooling all the regions to estimate the overall treatment effect may not be reasonable. Unlike the multiple center clinical trials conducted in the USA and Europe, in multi-regional clinical trials, different regional regulatory agencies may have their own ways to interpret data and approve new drugs. It is therefore practical to consider the case in which the data from the region with the minimal observed treatment effect is excluded from the analysis in order to attain the regulatory approval of the study drug. Under such cases, what is the appropriate statistical approach for the remaining regions? We provide a solution first formulated within the fixed effects framework and then extend it to discrete random effects models. Copyright © 2016 John Wiley & Sons, Ltd.

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Assessing the consistency of the treatment effect under the discrete random effects model in multiregional clinical trials

Cited by 13 publications

References 18 publications

Regional consistency and sample size considerations in a multiregional equivalence trial

Regional consistency and sample size considerations in a multiregional equivalence trial

An Analysis of Japanese Patients Enrolled in Multiregional Clinical Trials in Oncology

Inconsistency and drop‐minimum data analysis

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