Comparative effectiveness research

Methods 
  •  Hong H , Wang C, and Rosner G (2020). Meta-analysis of rare adverse events in randomized clinical
    trials: Bayesian and frequentist methods. Clinical Trials. 18(1):3-16. [Journal page]
  • Hong H, Fu H, and Carlin BP (2018). Power and commensurate priors for synthesizing aggregate and individual patient-level data in network meta-analysis. Journal of the Royal Statistical Society: Series C (Applied Statistics). 67(Part 4):1047-1069. [Journal page] 
  • 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. Statistical Methods in Medical Research. 26(5):2227-2243. [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]

Applications
  • Lopes RD, Hong H, Harskamp RE, Bhatt DL, Mehran R, Cannon CP, Granger CB, Verheugt FWA,
    Li J, Berg JMt, Sarafoff N, Gibson CM, and Alexander JH (2019). Safety and efficacy of antithrombotic
    strategies in patients with atrial fibrillation undergoing percutaneous coronary intervention: a
    network meta-analysis of randomized controlled trials. Journal of the American Medical Association Cardiology. 4(8):747-755. [Journal page]
  • Li T, Mayo-Wilson E, Fusco N, Hong H, Dickersin K, and the MUDS investigators. Caveat emptor: the combined effects on multiplicity and selective reporting (2018). Trials. 19(1):497. [Journal page]
  • Mayo-Wilson E, Li T, Fusco N, Bertizzolo L, Canner J, Cowley T, Doshi P, Ehmsen J, Gresham G, Guo N, Haythornthwaite J, Heyward J, Hong H, Lock D, Payne J, Rosman L, Stuart EA, Suarez-Cuervo C, Tolbert E, Twose C, Vedula S, and Dickersin K (2017). Cherry-picking by trialists and meta-analysts can drive conclusions about intervention efficacy. Journal of Clinical Epidemiology. 91:95-110. [Journal page]
  • Mayo-Wilson E, Fusco N, Li T, Hong H, Canner J, Dickersin K, and the MUDS team (2017). Multiple outcomes and analyses in clinical trials create challenges for interpretation and research synthesis. Journal of Clinical Epidemiology. 86:39-50. [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]