An Overview of Current Statistical Methods for Implementing Quality Tolerance Limits.

Background: In 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use updated its efficacy guideline for good clinical practice and introduced predefined quality tolerance limits (QTLs) as a quality control in clinical trials. QTLs are complementary to Quality by Design (QbD) principles (ICH-E8) and are one of the components of the risk-based clinical trial quality management system.

Methods: Currently the framework for QTLs process is well established, extensively describing the operational aspects of Defining, Monitoring and Reporting, but a single source of commonly used methods to establish QTLs and secondary limits is lacking.

A Cochran-Armitage-type and a score-free global test for multivariate ordinal data.

We propose a Cochran-Armitage-type and a score-free global test that can be used to assess the presence of an association between a set of ordinally scaled covariates and an outcome variable within the range of generalized linear models. Both tests are developed within the framework of the well-established 'global test' methodology and as such are feasible in high-dimensional data situations under any correlation and enable adjustment for covariates. The Cochran-Armitage-type test, for which an intimate connection with the traditional score-based Cochran-Armitage test is shown, rests upon explicit assumptions on the distances between the covariates' ordered categories.

Over-optimism in bioinformatics: an illustration.

Motivation: In statistical bioinformatics research, different optimization mechanisms potentially lead to 'over-optimism' in published papers. So far, however, a systematic critical study concerning the various sources underlying this over-optimism is lacking.

Results: We present an empirical study on over-optimism using high-dimensional classification as example.