Abstract
Many applications in modern biology measure a large number of genomic or proteomic covariates and are interested in assessing the impact of each of these covariates on a particular outcome of interest. In a study which follows a cohort of HIV-positive patients over time, for example, a researcher may genotype the virus infecting each patient to ascertain the presence or absence of a large number of mutations, in the hope of identifying mutations that affect how a patient’s plasma HIV RNA level (viral load) responds to a new drug regimen. Along with an estimate of the impact of each mutation on the time course of viral load, the researcher would generally like to have a measure of the statistical significance of these estimates in order to identify those mutations that are most likely to be genuinely related to the outcome. Such information could then be used to inform the decision of which drugs should be included in the regimen of a patient with a particular pattern of mutations.
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Bembom, O., Petersen, M.L., van der Laan, M.J. (2007). Identifying Important Explanatory Variables for Time-Varying Outcomes. In: Dubitzky, W., Granzow, M., Berrar, D. (eds) Fundamentals of Data Mining in Genomics and Proteomics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-47509-7_11
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DOI: https://doi.org/10.1007/978-0-387-47509-7_11
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