title: Method ruggedness studies incorporating a risk based approach: a tutorial.
abstract: This tutorial explains how well thought-out application of design and analysis methodology, combined with risk assessment, leads to improved assessment of method ruggedness. The authors define analytical method ruggedness as an experimental evaluation of noise factors such as analyst, instrument or stationary phase batch. Ruggedness testing is usually performed upon transfer of a method to another laboratory, however, it can also be employed during method development when an assessment of the method's inherent variability is required. The use of a ruggedness study provides a more rigorous method for assessing method precision than a simple comparative intermediate precision study which is typically performed as part of method validation. Prior to designing a ruggedness study, factors that are likely to have a significant effect on the performance of the method should be identified (via a risk assessment) and controlled where appropriate. Noise factors that are not controlled are considered for inclusion in the study. The purpose of the study should be to challenge the method and identify whether any noise factors significantly affect the method's precision. The results from the study are firstly used to identify any special cause variability due to specific attributable circumstances. Secondly, common cause variability is apportioned to determine which factors are responsible for most of the variability. The total common cause variability can then be used to assess whether the method's precision requirements are achievable. The approach used to design and analyse method ruggedness studies will be covered in this tutorial using a real example.
authors: Product Development and Statistical Sciences, GlaxoSmithKline Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK. phil.j.borman@gsk.com
authors: Phil J
authors: PJ
authors: Borman
authors: Marion J
authors: MJ
authors: Chatfield
authors: Ivana
authors: I
authors: Damjanov
authors: Patrick
authors: P
authors: Jackson
mesh_terms: D015203:Reproducibility of Results; D012107:Research Design; D018570:Risk Assessment