DOI QR코드

DOI QR Code

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

  • Kang, Seung-Ho (Department of Applied Statistics, Yonsei University) ;
  • Kim, Saemina (Department of Applied Statistics, Yonsei University)
  • 투고 : 2018.02.12
  • 심사 : 2018.05.14
  • 발행 : 2018.07.31

초록

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.

키워드

참고문헌

  1. Chen J, Quan H, Binkowitz B, Quyang SP, Tanaka Y, Li G, Menjoge S, and Ibia E (2010). Assessing consistent treatment effect in a multi-regional clinical trial: a systematic review, Pharmaceutical Statistics, 9, 242-253. https://doi.org/10.1002/pst.438
  2. Chen J, Quan H, Gallo P, et al. (2011). Consistency of treatment effect across regions in multi-regional clinical trials, part 1: design considerations, Drug Information Journal, 45, 595-602. https://doi.org/10.1177/009286151104500609
  3. Chen X, Lu N, Nair R, Xu Y, Kang C, Huang Q, Li N, and Chen H (2012a). Decision rules and associated sample size planning for regional approval utilizing multi-regional clinical trials, Journal of Biopharmaceutical Statistics, 22, 1001-1018. https://doi.org/10.1080/10543406.2012.701582
  4. Chen CT, Hung HMJ, and Hsiao CF (2012b). Design and evaluation of multi-regional trials with heterogeneous treatment effect across regions, Journal of Biopharmaceutical Statistics, 22, 1037-1050. https://doi.org/10.1080/10543406.2012.701585
  5. Hung HMJ, Wang SJ, and O'Neill RT (2010). Consideration of regional difference in design and analysis of multi-regional trials, Pharmaceutical Statistics, 9, 173-178. https://doi.org/10.1002/pst.440
  6. Ikeda K and Bretz F (2010). Sample size and proportion of Japanese patients in multi-regional trials, Pharmaceutical Statistics, 9, 207-216. https://doi.org/10.1002/pst.455
  7. International Conference on Harmonization (2006). Q&A for ICH E5 Guideline on Ethnic Factors in the Acceptability of Foreign Data.
  8. International Conference on Harmonization (2017). General Principles for Planning and Design of Multi-Regional Clinical Trials.
  9. Kawai N, Chung-Stein C, Komiyama O, and Li Y (2008). An approach to rationalize partitioning sample size into individual regions in a multi-regional trial, Drug Information Journal, 42, 139-147. https://doi.org/10.1177/009286150804200206
  10. Ko FS, Tsou HH, Liu JP, and Hsiao CF (2010). Sample size determination for a specific region in a multi-regional trial, Journal of Biopharmaceutical Statistics, 24, 870-885.
  11. Liu JP, Chow SC, and Hsiao CF (2013). Design and Analysis of Bridging Studies, CRC Press, Boca Raton, FL.
  12. Ministry of Health, Labor, and Welfare of Japan (2007). Basic Principles on Global Clinical Trials, from: http://www.pmda.go.jp/english/service/pdf/notications/0928010-e.pdf
  13. Quan H, Li M, Chen J, et al. (2010a). Assessment of consistency of treatment effects in multiregional clinical trials, Drug Information Journal, 44, 617-632. https://doi.org/10.1177/009286151004400509
  14. Quan H, Zhao PL, Zhang J, Roessner M, and Aizawa K (2010b). Sample size considerations for Japanese patients in a multi-regional trial based on MHLW guidance, Pharmaceutical Statistics, 9, 100-112.
  15. Quan H, Li M, Shih WJ, Ouyang SP, Chen J, Zhang J, and Zhao P (2013). Empirical shrinkage estimator for consistency assessment of treatment effects in MRCT, Statistics in Medicine, 32, 1691-1706. https://doi.org/10.1002/sim.5543
  16. Quan H, Mao X, Chen J, Shih WJ, Ouyang SP, Zhang J, Zhao PL, and Binkowitz B (2014). Multi-regional clinical trial design and consistency assessment of treatment effects, Statistics in Medicine, 33, 2191-2205. https://doi.org/10.1002/sim.6108
  17. Tsong Y, Chang WJ, Dong X, and Tsou HH (2012). Assessment of regional treatment effect in a multi-regional clinical trial, Journal of Biopharmaceutical Statistics, 22, 1019-1036. https://doi.org/10.1080/10543406.2012.701583
  18. Tsou HH, Chow SC, Lan KKG, et al. (2010). Proposals of statistical consideration to evaluation of results for a specific region in multi-regional trials - Asian perspective, Pharmaceutical Statistics, 9, 201-206. https://doi.org/10.1002/pst.442
  19. Tsou HH, Hung HMJ, Chen YM, Huang WS, Chang WJ, and Hsiao CF (2012). Establishing consistency across all regions in a multi-regional clinical trial, Pharmaceutical Statistics, 11, 295-299. https://doi.org/10.1002/pst.1512
  20. Uesaka U (2009). Sample size allocation to regions in a multi-regional trial, Journal of Biopharmaceutical Statistics, 19, 580-594. https://doi.org/10.1080/10543400902963185