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Review
. 2014 Jan;1844(1 Pt A):145-55.
doi: 10.1016/j.bbapap.2013.07.018. Epub 2013 Aug 9.

A practical guide to epidemiological practice and standards in the identification and validation of diagnostic markers using a bladder cancer example

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Review

A practical guide to epidemiological practice and standards in the identification and validation of diagnostic markers using a bladder cancer example

T Behrens et al. Biochim Biophys Acta. 2014 Jan.

Abstract

Technical advances to analyze biological markers have generated a plethora of promising new marker candidates for early detection of cancer. However, in subsequent analyses only few could be successfully validated as being predictive, clinically useful, or effective. This failure is partially due to rapid publication of results that were detected in early stages of biomarker research. Methodological considerations are a major concern when carrying out molecular epidemiological studies of diagnostic markers to avoid errors that increase the potential for bias. Although guidelines for conducting studies and reporting of results have been published to improve the quality of marker studies, their planning and execution still need to be improved. We will discuss different sources of bias in study design, handling of specimens, and statistical analysis to illustrate possible pitfalls associated with marker research, and present legal, ethical, and technical considerations associated with storage and handling of specimens. This article presents a guide to epidemiological standards in marker research using bladder cancer as an example. Because of the possibility to detect early cancer stages due to leakage of molecular markers from the target organ or exfoliation of tumor cells into the urine, bladder cancer is particularly useful to study diagnostic markers. To improve the overall quality of marker research, future developments should focus on networks of studies and tissue banks according to uniform legal, ethical, methodological, and technical standards. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Keywords: Bias; Biomarker; Confounder; Epidemiology; Methodology; PRoBE.

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