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 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.

KEYWORDS:

• Multiregional clinical trial
• outlier detection
• influence diagnosis
• leave-one-out cross-validation
• bootstrap

Data availability statement

The MRCT datasets used in Section 3 are parts of the published data from Food and Drug Administration [29] and Pharmaceuticals and Medical Devices Agency [32].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (grant numbers: JP18K11187, JP19H04074).

Notes on contributors

Makoto Aoki

Makoto Aoki is a graduate student at the Department of Statistical Science, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, Tokyo, Japan. He is also a senior biostatistician at the Integrated Biostatistics Department, Novartis Pharma K. K., Tokyo, Japan.

Hisashi Noma

Hisashi Noma is an associate professor at the Department of Data Science, The Institute of Statistical Mathematics, Tokyo, Japan.

Masahiko Gosho

Masahiko Gosho is a professor at the Department of Biostatistics, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.