(416g) Biomarker Identification in Autism Spectrum Disorder: Common Pitfalls and Emerging Strategies
Distributions of biomarkers in the diseased and healthy cohorts are typically described by measures of central tendency and some measure of the dispersion or range. Furthermore, the most commonly reported difference in the biomarker distributions for diseased and healthy cohorts is a difference in the measure of central tendency . However, this âstatistical significanceâ is usually misinterpreted as being synonymous with âclinical significanceâ. Instead, diagnostic biomarkers should be assessed on the separability of the diseased and healthy distributions (e.g. sensitivity/specificity, receiver-operating-characteristic curve, etc.) in order to achieve clinically-relevant biomarkers.
Potential biomarkers usually arise from discussions of or mathematical modeling of a biological network and how this network connects to the phenomenon of interest. Traditionally, only single measurements or metrics are used to construct a potential biomarker and the remaining portions of the underlying network is ignored . While this approach can work well for simpler diseases, more complex diseases lacking a completely understood disease mechanism (e.g., neurological diseases) can benefit from incorporating multiple measurements into the potential biomarker without specific relationships between multiple measurements imposed a priori , .
These issues with current biomarker reporting/development are highlighted through case studies of biomarker identification from Autism Spectrum Disorder (ASD). It is shown that effective biomarkers can be developed from multiple measurements and that p-Values from Studentâs t-tests can lead to incorrect conclusions regarding biomarker quality. In comparison to this, multivariate statistical analysis and modeling techniques can result in the identification of biomarkers that can be used for disease classification and severity prediction even when univariate approaches fail .
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