title: Inconsistency and drop-minimum data analysis.
abstract: 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.
authors.affiliation: Janssen Research & Development, Johnson & Johnson, 920 Rt 202 S., Raritan, NJ, 08869, U.S.A.
authors.forename: Fei
authors.initials: F
authors.lastname: Chen
authors.affiliation: Janssen Research & Development, Johnson & Johnson, 920 Rt 202 S., Raritan, NJ, 08869, U.S.A.
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.affiliation: Janssen Research & Development, Johnson & Johnson, 920 Rt 202 S., Raritan, NJ, 08869, U.S.A.
authors.forename: K K Gordon
authors.initials: KK
authors.lastname: Lan
mesh_terms: D003627:Data Interpretation, Statistical; D017277:Drug Approval / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000379:methods; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D016896:Treatment Outcome
keywords: discrete random effects model; fixed effects model; multi-regional clinical trials