Dr. Kuo has studied patterns of care, treatment toxicities, and health outcomes using a variety of large administrative data sets in the past ten years. She has applied analytical methods in her previous studies to model changes in continuous and discrete outcomes over time (autoregressive integrated moving average (ARIMA) models, piece-wise regression models, spline regression models), to study the effect of covariates in longitudinal data (generalized estimated equation models, linear mixed models), to assess the variation of care across different levels of health care providers and different geographic levels (hierarchical linear models, hierarchical generalized linear mix models), and to examine the impact of covariates changes on time-to-event data (proportional hazard model with time-dependent covariates). For comparative effectiveness studies using observational data, she has also used different matching and analytical methods (conditional logit models, propensity models, instrumental variable analyses, sensitivity analyses) in various studies. Dr. Kuo has developed an independent research focus that evolved from collaborative studies requiring new statistical methodology.
Since 2008, she has developed a particular interest in studying health care delivery systems. In the past four years, her research has focused on the study of hospitalist care and its impact. With her nursing background, she extends her research to examine the use and effectiveness of primary care provided by nurse practitioners in communities and nursing homes. She is currently the PI of an R01 funded by AHRQ to study nurse practitioner cares.