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Research Article

Methods for detecting outlying regions and influence diagnosis in multi-regional clinical trials

ORCID Icon, ORCID Icon & ORCID Icon
Pages 30-48 | Received 12 Feb 2020, Accepted 22 Apr 2021, Published online: 19 May 2021

References

  • Quan H, Li M, Shih WJ, et al. Empirical shrinkage estimator for consistency assessment of treatment effects in multi-regional clinical trials. Stat Med. 2013;32(10):1691–1706.
  • International Conference Harmonization. General principles for planning and design of multi-regional clinical trials. 2017. Available at: https://database.ich.org/sites/default/files/E17EWG_Step4_2017_1116.pdf.
  • Quan H, Mao X, Tanaka Y, et al. Example-based illustrations of design, conduct, analysis and result interpretation of multi-regional clinical trials. Contemp Clin Trials. 2017;58:13–22.
  • Chen J, Quan H, Binkowitz B, et al. Assessing consistent treatment effect in a multi-regional clinical trial: a systematic review. Pharm Stat. 2010;9(3):242–253.
  • Quan H, Zhao PL, Zhang J, et al. Sample size considerations for Japanese patients in a multi-regional trial based on MHLW guidance. Pharm Stat. 2010;9(2):100–112.
  • Chen J, Quan H, Gallo P, et al. Consistency of treatment effect across regions in multiregional clinical trials, part 1: design considerations. Drug Info J. 2011;45(5):595–602.
  • Liu JP, Chow SC, Hsiao CF. Design and analysis of bridging studies. Boca Raton (FL): CRC Press; 2012.
  • Tsou HH, James Hung HM, Chen YM, et al. Establishing consistency across all regions in a multi-regional clinical trial. Pharm Stat. 2012;11(4):295–299.
  • Quan H, Mao X, Chen J, et al. Multi-regional clinical trial design and consistency assessment of treatment effects. Stat Med. 2014;33(13):2191–2205.
  • Guo H, Chen J, Quan H. Evaluation of local treatment effect by borrowing information from similar countries in multi-regional clinical trials. Stat Med. 2016;35(5):671–684.
  • Liu JT, Tsou HH, Gordon Lan KK, et al. Assessing the consistency of the treatment effect under the discrete random effects model in multiregional clinical trials. Stat Med. 2016;35(14):2301–2314.
  • Diao G, Zeng D, Ibrahim JG, et al. Statistical design of noninferiority multiple region clinical trials to assess global and consistent treatment effects. J Biopharm Stat. 2017;27(6):933–944.
  • Teng Z, Lin J, Zhang B. Practical recommendations for regional consistency evaluation in multi-regional clinical trials with different endpoints. Stat Biopharm Res. 2017;10(1):50–56.
  • Kawai N, Chuang-Stein C, Komiyama O, et al. An approach to rationalize partitioning sample size into individual regions in a multiregional trial. Drug Info J. 2008;42(2):139–147.
  • Uesaka H. Sample size allocation to regions in a multiregional trial. J Biopharm Stat. 2009;19(4):580–594.
  • Ikeda K, Bretz F. Sample size and proportion of Japanese patients in multi-regional trials. Pharm Stat. 2010;9(3):207–216.
  • Ko FS, Tsou HH, Liu JP, et al. Sample size determination for a specific region in a multiregional trial. J Biopharm Stat. 2010;20(4):870–885.
  • Belsley DA, Kuh E, Welsch RE. Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley; 1980.
  • DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–188.
  • Chen X, Lu N, Nair R, et al. Decision rules and associated sample size planning for regional approval utilizing multiregional clinical trials. J Biopharm Stat. 2012;22(5):1001–1018.
  • Hung HM, Wang SJ, O'Neill RT. Consideration of regional difference in design and analysis of multi-regional trials. Pharm Stat. 2010;9(3):173–178.
  • Hedges LV, Olkin I. Statistical methods for meta-analysis. Orlando: Academic Press; 1985.
  • Viechtbauer W, Cheung M W. Outlier and influence diagnostics for meta-analysis. Res Synth Methods. 2010;1(2):112–125.
  • Gumedze FN, Jackson D. A random effects variance shift model for detecting and accommodating outliers in meta-analysis. BMC Med Res Methodol. 2011;11:19.
  • Negeri ZF, Beyene J. Statistical methods for detecting outlying and influential studies in meta-analysis of diagnostic test accuracy studies. Stat Methods Med Res. 2019. doi:10.1177/0962280219852747.
  • Noma H, Gosho M, Ishii R, et al. Outlier detection and influence diagnostics in network meta-analysis. 2019; arXiv: 1910.13080.
  • Lin DY, Zeng D. On the relative efficiency of using summary statistics versus individual-level data in meta-analysis. Biometrika. 2010;97(2):321–332.
  • Zeng D, Lin DY. On random-effects meta-analysis. Biometrika. 2015;102(2):281–294.
  • Food and Drug Administration. Cardiovascular and Renal Drugs Advisory Committees and Meeting Materials. 2009. Available at: https://wayback.archive-it.org/7993/20170405212708/https://www.fda.gov/downloads/AdvisoryCommittees/CommitteesMeetingMaterials/Drugs/CardiovascularandRenalDrugsAdvisoryCommittee/UCM138385.pdf.
  • Turpie AG, Lassen MR, Eriksson BI, et al. Rivaroxaban for the prevention of venous thromboembolism after hip or knee arthroplasty. Pooled analysis of four studies. Thromb Haemost. 2011;105(3):444–453.
  • Brenner BM, Cooper ME, de Zeeuw D, et al. Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N Engl J Med. 2001;345(12):861–869.
  • Pharmaceuticals and Medical Devices Agency. Review report for Losartan. 2006; Available at: pmda.go.jp/drugs/2006/P200600021/63015300_21000AMZ00678_Q101_1.pdf.
  • Kim S, Kang S-H. Hierarchical linear models for multiregional clinical trials. Stat Biopharm Res. 2020;12(3):1–10.
  • Higgins J, Thomas J. Cochrane handbook for systematic reviews of interventions. 2nd edn Chichester: Wiley; 2019.
  • Efron B, Tibshirani R. An Introduction to the bootstrap. New York: Chapman & Hall; 1994.
  • Veroniki AA, Jackson D, Bender R, et al. Methods to calculate uncertainty in the estimated overall effect size from a random-effects meta-analysis. Res Synth Methods. 2019;10(1):23–43.
  • Ying L, Song F, Chow SC, et al. On evaluation of consistency in multi-regional clinical trials. J Biopharm Stat. 2018;28(5):840–856.
  • Stein MC, da Silva MF, Duczmal LH. Alternatives to the usual likelihood ratio test in mixed linear models. Comp Stat Data Anal. 2014;69:184–197.
  • Noma H, Nagashima K, Maruo K, et al. Bartlett-type corrections and bootstrap adjustments of likelihood-based inference methods for network meta-analysis. Stat Med. 2018;37(7):1178–1190.
  • Ukyo Y, Noma H, Maruo K, et al. Improved small sample inference methods for a mixed-effects model for repeated measures approach in incomplete longitudinal data analysis. Stats. 2019;2(2):174–188.

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