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65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS)

06.09. - 09.09.2020, Berlin (online conference)

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A systematic review on the use of methods for left-censored biomarker data

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  • Dominik Thiele - Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, GermanyAirway Research Center North (ARCN), Member of the German Center for Lung Research (DZL, Germany
  • Inke R. König - Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, GermanyAirway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS). Berlin, 06.-09.09.2020. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 437

doi: 10.3205/20gmds361, urn:nbn:de:0183-20gmds3610

Published: February 26, 2021

© 2021 Thiele et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

Text

Classifying patients into subgroups in precision medicine strongly relies on the availability of biomarker data like gene expression profiles. Although there is a huge amount of candidate data, finding suitable profiles still is challenging due to the lack of reproducibility and statistical power. In addition, data is frequently left-censored, and it is yet unclear how best to handle data where a non-negligible proportion has values under a given detection limit. In fact, many approaches have been suggested in the literature that differ with regard to assumptions and application settings. Also, they have been investigated in different settings as defined, for instance by sample sizes, percentage of non-detects, and the underlying distribution of the data, making the practical choice difficult In this work we therefore target this issue by summarizing published theoretical and simulation studies in which methods for the analysis of left-censored data are suggested and compared with each other. We performed a systematic review following the PRISMA statement to derive an overview of the existing methods and their applicability in different settings. The results will help to guide researchers to select the most suitable method for a specific application.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.