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.
- Hong H, Chu H, Zhang J, and Carlin BP (2016). A bayesian missing data framework for generalized multiple outcome mixed treatment comparisons. (with discussion and rejoinder). Research Synthesis Methods. 7(1):6-22. [Journal page, discussion, rejoinder]
- Zhang J, Chu H, Hong H, Neaton JD, Virnig BA, and Carlin BP (2015). Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness. To appear. Statistical Methods in Medical Research. doi:10.1177/0962280215596185. [Journal page]
- Hong H, Fu H, Price KL, Carlin BP (2015). Incorporation of individual patient data in network meta-analysis for multiple continuous endpoints, with application to diabetes treatment. Statistics in Medicine. 34(20):2794-2819. [Journal page]
- Ohlssen D, Price KL, Xia HA, Hong H, Kerman J, Fu H, Quartey G, Heilmann CR, Ma H, and Carlin BP (2014). Guidance on the implementation and reporting of a drug safety Bayesian network meta-analysis. Pharmaceutical Statistics. 13(1):55-70. [Journal page]
- Hong H, Carlin BP, Shamliyan T,Wyman JF, Ramakrishnan R, Sainfort F, and Kane RL (2013). Comparing Bayesian and frequentist approaches for multiple outcome mixed treatment comparisons. Medical Decision Making. 33(5):702-714. [Journal page]
- Li T, Lindsey K, Rouse B, Hong H, Shi Q, Friedman DS, Wormald R, and Dickersin K (2016). Comparative effectiveness of first-line medications for primary open angle glaucoma - A systematic review and network meta-analysis. Ophthalmology. 123(1):129-140. [Journal page]
- Mayo-Wilson E, Hutfless S, Li T, Gresham G, Fusco N, Ehmsen J, Heyward J, Vedula S, Lock D, Haythornthwaite J, Payne JL, Cowley T, Rosman L, Twose C, Stuart EA, Hong H, Doshi P, Suarez-Cuervo C, Singh S, and Dickersin K (2015). Integrating multiple data sources (MUDS) for meta-analysis to improve patient-centered outcomes research: a protocol for a systematic review. Systematic reviews. 4(1):1. [Journal page]
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.
- Hong H, Rudolph K, and Stuart EA (2016). Bayesian approach for addressing differential covariate measurement error in propensity score methods. To appear. Psychometrika.
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.
- Shippee TP, Hong H, Kane RL, and Henning-Smith C (2015). Longitudinal changes in nursing home resident-reported quality of life: The role of facility characteristics. Research on Aging. 37(6):555-580. [Journal page]
- Wester WC, Koethe JR, Shepherd BE, Stinnette SE, Rebeiro PF, Kipp AM, Hong H, Bussmann H, Gaolathe T, McGowan CC, Sterling TR, and Marlink RG (2011). Non-AIDS-defining events among HIV-1-infected adults receiving combination antiretroviral therapy in resource-replete versus resourcelimited urban setting. AIDS. 25(12):1471-1479. [Pubmed page]