Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Methods for detecting outlying regions and influence diagnosis in multi-regional clinical trials Due to the globalization of drug development, multi-regional clinical trials (MRCTs) have been increasingly adopted in clinical evaluations. In MRCTs, the primary objective is to demonstrate the efficacy of new drugs in all participating regions, but heterogeneity of various relevant factors across these regions can cause inconsistency of treatment effects. In particular, outlying regions with extreme profiles can influence the overall conclusions of these studies. In this article, we propose quantitative methods to detect these outlying regions and to assess their influences in MRCTs. The approaches are as follows: (1) a method using a dfbeta-like measure, a studentized residual obtained by a leave-one-out cross-validation (LOOCV) scheme; (2) a model-based significance testing method using a mean-shifted model; (3) a method using a relative change measure for the variance estimate of the overall effect estimator; and (4) a method using a relative change measure for the heterogeneity variance estimate in a random-effects model. Parametric bootstrap schemes are proposed to accurately assess the statistical significance and variabilities of the aforementioned influence diagnostic tools. We illustrate the effectiveness of these proposed methods via applications to two MRCTs, the RECORD and RENAAL studies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

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

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

Abstract

Due to the globalization of drug development, multi-regional clinical trials (MRCTs) have been increasingly adopted in clinical evaluations. In MRCTs, the primary objective is to demonstrate the efficacy of new drugs in all participating regions, but heterogeneity of various relevant factors across these regions can cause inconsistency of treatment effects. In particular, outlying regions with extreme profiles can influence the overall conclusions of these studies. In this article, we...
Loading next page...
 
/lp/taylor-francis/methods-for-detecting-outlying-regions-and-influence-diagnosis-in-d0hv80tE6F
Publisher
Taylor & Francis
Copyright
© 2021 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2021.1921944
Publisher site
See Article on Publisher Site

Abstract

Due to the globalization of drug development, multi-regional clinical trials (MRCTs) have been increasingly adopted in clinical evaluations. In MRCTs, the primary objective is to demonstrate the efficacy of new drugs in all participating regions, but heterogeneity of various relevant factors across these regions can cause inconsistency of treatment effects. In particular, outlying regions with extreme profiles can influence the overall conclusions of these studies. In this article, we propose quantitative methods to detect these outlying regions and to assess their influences in MRCTs. The approaches are as follows: (1) a method using a dfbeta-like measure, a studentized residual obtained by a leave-one-out cross-validation (LOOCV) scheme; (2) a model-based significance testing method using a mean-shifted model; (3) a method using a relative change measure for the variance estimate of the overall effect estimator; and (4) a method using a relative change measure for the heterogeneity variance estimate in a random-effects model. Parametric bootstrap schemes are proposed to accurately assess the statistical significance and variabilities of the aforementioned influence diagnostic tools. We illustrate the effectiveness of these proposed methods via applications to two MRCTs, the RECORD and RENAAL studies.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Jan 2, 2021

Keywords: Multiregional clinical trial; outlier detection; influence diagnosis; leave-one-out cross-validation; bootstrap

References