Survival analysis with high-dimensional covariates, with applications to cancer genomics

Survival analysis with high-dimensional covariates, with applications to cancer genomics

by Sihai Dave Zhao

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Recent technological advances have given cancer researchers the ability to gather vast amounts of genetic and genomic data from individual patients. These offer tantalizing possibilities for, for example, basic cancer biology, tailored therapies, and personalized risk predictions. At the same time, they have also introduced many analytical difficulties that cannot be properly addressed with current statistical procedures, because the number of genomic covariates in these datasets is often larger than the sample size. In this dissertation we study methods for addressing this so-called high-dimensional issue when genomic data are used to analyze time-to-event outcomes, so common to clinical cancer studies.

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    How does the tension between the vastness of genetic data and the limitations of traditional statistical methods mirror other challenges we face in modern, information-heavy fields?

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    In an era where personalized medicine promises tailored therapies, how do you personally balance the desire for precise, data-driven health predictions with the inherent uncertainty of biological systems?

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    The research tackles the "high-dimensional issue" where variables outnumber the sample size; how do you manage and make decisions in your own life when overwhelmed by too many variables or choices?

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