Affiliations: CSIRO Mathematical and Information Sciences, Glen Osmond, Australia | Biostatistics Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
Correspondence:
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Corresponding author. Private Mail Bag No 2, Glen Osmond, South Australia 5064, Australia.
Abstract: The statistical properties required for effective biomarkers for disease are examined. It is shown that an “effectiveness parameter” D can be calculated that summarises the performance of a given biomarker and can distinguish between effective and ineffective biomarkers. D can be readily calculated from published summaries of biomarker levels and provides a simpler alternative to the commonly used “Area under the Curve” statistic. The impact of within-individual and between-individual variation in biomarker levels is also evaluated. An approach to the choice of sample size for experiments to estimate D is described.
