title: Graphical assessment of consistency in treatment effect among countries in multi-regional clinical trials.
abstract: BACKGROUND
  One key objective of a multi-regional clinical trial (MRCT) is to use the trial results to 'bridge' from the global level to local region in support of local registrations. However, data from each individual country are typically limited and the large number of countries will increase the chance of false positive findings.
  
  
  OBJECTIVE
  Graphical tools to facilitate identification of potential outlying countries could be useful for country-level assessment. Existing methods such as funnel plot and expected range of treatment effect can substantially increase the false positive rate. The expected range approach can also have a very low power when there are a large number of small countries, which is typical in a MRCT.
  
  
  METHODS
  In this article, we apply normal probability plots, commonly used as a diagnostic tool in linear regression analysis, to assess the differences among countries. Evidence of possible inconsistency, which incorporates both the estimated treatment effect and sample size, is plotted against its expected order statistic.
  
  
  RESULTS
  A simulation study is conducted to assess the impact of the negative correlation among residuals due to unequal sample sizes among countries and the performance of the proposed methods compared to existing approaches. The proposed methods tend to have a balanced consideration with substantially smaller false positive rate and reasonable probability to identify outlying countries in realistic scenarios.
  
  
  CONCLUSIONS
  While much lower than that of commonly used methods, the false positive rates of the proposed methods are not strictly controlled. This may be acceptable for these graphical tools with intention to flag potential outliers for investigation.
  
  
  CONCLUSIONS
  We recommend routine use of normal probability plots in MRCTs as a tool to identify potential outliers. If the normal probability plot is approximately linear but has heavy tails with a few outlying countries, these potential outliers should be examined carefully to understand the possible reasons.
authors.affiliation: aMerck Research Laboratories, Rahway, NJ, USA.
authors.forename: Joshua
authors.initials: J
authors.lastname: Chen
authors.forename: Hao
authors.initials: H
authors.lastname: Zheng
authors.forename: Hui
authors.initials: H
authors.lastname: Quan
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.forename: Paul
authors.initials: P
authors.lastname: Gallo
authors.forename: Soo Peter
authors.initials: SP
authors.lastname: Ouyang
authors.forename: Bruce
authors.initials: B
authors.lastname: Binkowitz
authors.forename: Naitee
authors.initials: N
authors.lastname: Ting
authors.forename: Yoko
authors.initials: Y
authors.lastname: Tanaka
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Luo
authors.forename: Ekopimo
authors.initials: E
authors.lastname: Ibia
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods*; D006801:Humans; D007391:International Cooperation; D016014:Linear Models; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000379:methods*; D017063:Outcome Assessment, Health Care; D012107:Research Design; D018401:Sample Size; D013223:Statistics as Topic / Q000379:methods*