I'm a biostatistical research scientist, wrapping up a postdoc at the Stanford Prevention Research Center, where my colleagues and I focus on health promotion and disease prevention. I am the principal investigator of a pilot project called "Improving Personalized Medicine through N-of-1 Causal Inference and Predictive Modeling", funded by the Stanford Center for Clinical and Translational Research and Education (Spectrum). (We are developing analytical methods for causal prediction of migraine headache attacks, with future applicability to many other chronic conditions.)
My research focuses on trying to answer personalized causal questions using self-tracked data, particularly for chronic conditions. For example: "If I change X (e.g., exercise or eating habit, other health-related behavior, drug or supplement combination), will this affect my Y (e.g., weight, blood glucose level, microbiome measurement, biomarker, migraine frequency)?" (Note that this is slightly different from the question "Does X affect Y?" in that the former conveys an intent to self-test a hypothesized cause, while the latter is a more general question.)
Can I help advance your organization's personalized-health projects or initiatives, by innovating and applying these methods to help self-trackers analyze their own data? If you've got an opportunity (academia or industry) that might be a fit, feel free to contact me at email@example.com, or to forward my information. More on me, including my CV/resume: https://profiles.stanford.edu/ericjdaza, https://www.linkedin.com/in/ericjdaza/. Let's do this!
Eric J. Daza, DrPH, MPS
Postdoctoral Research Fellow
Stanford Prevention Research Center