Research

Funding Resource

Current:

PCORI (ME-2024C1-37433): Learning Interpretable and Fair Personalized Treatment Rules (05/01/2025 – 01/31/2029, Total Amount: $1,050,000)
NIH (R01AI192972): Novel Integrative Analysis of Microbiome and Host Genetics in Childhood Asthma Research (07/01/2025 - 06/30/2030, Total Amount: $2,989,520)
NSF (DMS-2515263) Collaborative Research: Distributional Balancing Methods for Advancing Causal Inference in Complex Settings (09/01/2025 - 08/30/2028, Total Amount: $68,000)

Past:

PCORI (ME-2018C2-13180): Validating and generalizing personalized treatment rules by leveraging different data sources (09/01/2019 – 06/30/2022, Total Amount: $730,312)
NSF (DMS-2054346): Unraveling the Role of Human Microbiome to Advance Precision Medicine (09/01/2021 – 08/30/2025,Total Amount: $599,980)

Research Summary

Precision medicine targets delivering the right treatment to the right people at the right time. Guanhua's research focuses on developing biostatistics and biomedical informatics methods for analyzing complex biomedical data to advance precision medicine. There are two main areas: treatment decisions and treatment discoveries.

When multiple treatments are available, then the goal is to help patients and physicians to decide among them. As an alternative to the "one-size-fits-all" treatment strategy, we could recommend treatments using demographic information, health history, and genetic markers based on individualized treatment rules (ITRs). Estimating ITRs is not only an important topic for precision medicine but also closely related to causal inference. He has proposed new methods for doing so under various types of treatments (binary, continuous) and outcomes (censored, zero-inflated).
When there are no suitable treatment options for a disease, we need to understand its risk factors to estimate ITRs. He has built risk prediction models using patients' electronic health records (EHRs) as well as exploring disease-biomarkers association using -omics data. Both are informative for treatment discovery.

Openings

We always welcome highly motivated graduate students to join our group, please email Guanhua to discuss potential topics.

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