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Guidelines for improving statistical analyses of validation datasets for plant pest diagnostic tests

Authors :
Massart, Sebastien
Lebas, Benedicte
Chabirand, Aude
Chappé, Anne Marie
Dreo, Tanja
Faggioli, Francesco
Harrison, Catherine
Macarthur, Roy
Mehle, Natasha
Mezzalama, Monica
Petter, Françoise
Ravnikar, Maja
Renvoisé, Jean Philippe
Spadaro, Davide
Tomassoli, Laura
Tomlinson, Jenny
Trontin, Charlotte
van der Vlugt, René
Vučurović, Ana
Weekes, Rebecca
Brostaux, Yves
Massart, Sebastien
Lebas, Benedicte
Chabirand, Aude
Chappé, Anne Marie
Dreo, Tanja
Faggioli, Francesco
Harrison, Catherine
Macarthur, Roy
Mehle, Natasha
Mezzalama, Monica
Petter, Françoise
Ravnikar, Maja
Renvoisé, Jean Philippe
Spadaro, Davide
Tomassoli, Laura
Tomlinson, Jenny
Trontin, Charlotte
van der Vlugt, René
Vučurović, Ana
Weekes, Rebecca
Brostaux, Yves
Source :
ISSN: 0250-8052
Publication Year :
2022

Abstract

Appropriate statistical analysis of the validation data for diagnostic tests facilitates the evaluation of the performance criteria and increases the confidence in the conclusions drawn from these data. A comprehensive approach to analysing and reporting data from validation studies and inter-laboratory comparisons such as test performance studies is described. The proposed methods, including statistical analyses, presentation and interpretation of the data, are illustrated using a real dataset generated during a test performance study conducted in the framework of the European project, VALITEST. This analytical approach uses, wherever possible and whenever applicable, statistical analyses recommended by international standards illustrating their application to plant health diagnostic tests. The present work is addressed to plant health diagnosticians and researchers interested and/or involved in the validation of plant diagnostic tests, and also aims to convey the necessary information to those without a statistical background. Detailed statistical explanations are provided in the Appendices.

Details

Database :
OAIster
Journal :
ISSN: 0250-8052
Notes :
application/pdf, EPPO Bulletin 52 (2022) 2, ISSN: 0250-8052, ISSN: 0250-8052, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1350175611
Document Type :
Electronic Resource