Comparative effectiveness research
- Which interventions work best?
Comparative effectiveness research is growing in importance in public health and medicine as interest in patient-centered outcome research and personalized medicine is increasing. My research focus on developing Bayesian hierarchical models for network meta-analysis. Network meta-analysis compares the effectiveness (and/or safety) of multiple treatments in a principled way to find the best treatment.
Causal inference and measurement error
- Does your data measure covariates correctly?
Non-experimental studies are used to estimate causal effects, but they are not perfect; for example, they often measure some covariates with error (e.g., self-reported income) and use different measurement between treated and control groups. These measurement errors could lead to biased estimates of treatment effects. I have contributed to developing propensity score methods under the Bayesian framework for handling complex covariate measurement error structures.
Generalizability of study findings to target populations
- What is the population average treatment effect?
The external validity of randomized controlled trials and how to generalize the results of trials to target populations are an important issue especially when we want to translate the results to community settings. I have recently started to develop methods that bridge the two fields, meta-analysis and generalizability. This work will provide guidance on how to draw population inferences from meta-analysis, an important practical question of interest in public health.
In addition to the major methodological developments described above, I have been involved in several applied projects in diverse areas of research, and greatly enjoy the opportunity to work with collaborative teams and develop methodological projects directly motivated by real world problems.