DESCRIPTION (provided by applicant): I am a PhD statistician who specializes in observational studies and comparative effectiveness research (CER). I have just completed the first year of a three-year fellowship in the statistics department of Stanford University. My long-term goal is to become a tenure track professor in a health services, biostatistics or statistics department. Project 1: Make an instrumental variable technique, known as "near-far matching," more accessible to CER researchers. Near-far matching is similar to propensity score matching, but is capable of estimating unbiased treatment effects even when there is confounding caused by unobserved covariates. Project 2: Combine causal inference techniques (i.e., ways to obtain unbiased treatment effects) with cutting edge predictive modeling techniques (e.g., nonparametric, fast algorithms for discovering interesting parts of the covariate space). The proposed techniques can use observational data to identify subpopulations which have large (or very small) responses to a given treatment (a.k.a. "treatment effect heterogeneity"). Interestingly, we propose a prediction technique which is still valid when there is unobserved confounding. Project 3: Propose an empirical Bayes technique which uses patient-level information (e.g., demographic covariates) plus patient-level longitudinal experience (e.g., repeated measurements of HA1c or pain scores) to enhance prognostic ability for chronic care patients. I have assembled an interdisciplinary team of scientist from Stanford University who are highly committed to my career development. My primary mentor is Phil Lavori PhD, the chair of the Department of Health Research and Policy. My co-mentor is Mark Cullen MD, chief of the Division of General Medical Disciplines in Stanford Medical School. In addition to my mentors, I have four advising consultants: two statistician - Trevor Hastie and Tze Lai; two health economists - Victor Fuchs and Jay Bhattacharya. I have specific projects I will work on with each of these researchers. These projects require regular meetings between me and each of my supporters. Much of my mentoring will come through an apprenticeship model of collaboration. The career development program outlined in this application contains formal mentorship, didactic coursework, and seminars structured around three areas: (1) predictive modeling, (2) longitudinal analysis and (3) health disparities and health systems. During the first
year of the K99 phase I will audit three classes that specifically address these three topics. Stanford has very active statistics and CER groups; I will attend several regularly reoccurring workshops and seminars in order to build my experience with these research communities. Additionally, I will have biweekly meetings with Professor Cullen's Alcoa Study research group - led by Professor Cullen and consisting of postdocs, research fellows, and junior faculty discussing research issues.
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