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Displaying 1 through 6 of 6 publications.
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Conover MM, Rothman KJ, Sturmer T, Ellis AR, Poole C, Jonsson Funk M. Propensity score trimming mitigates bias due to covariate measurement error in inverse probability of treatment weighted analyses: a plasmode simulation. Stat Med. 2021 Apr;40(9):2101-12. doi: 10.1002/sim.8887
Webster-Clark M, Sturmer T, Wang T, Man K, Marinac‐Dabic D, Rothman KJ, Ellis AR, Gokhale M, Lunt M, Girman C, Glynn RJ. Using propensity scores to estimate effects of treatment initiation decisions: state of the science. Stat Med. 2021 Mar 30;40(7):1718-35. doi: 10.1002/sim.8866
McSweeney L, Breckons M, Fattakhova G, Oluboyede Y, Vale L, Ternent L, Balp M-M, Doward L, Brass CA, Beyer F, Sanyal A, Anstee QM. Health-related quality of life and patient-reported outcome measures in NASH-related cirrhosis. JHEP Rep. 2020 Mar 6;2(3). doi: 10.1016/j.jhepr.2020.100099
Quintana M, Shrader J, Slota C, Joe G, McKew JC, Fitzgerald M, Gahl WA, Berry S, Carrillo N. Bayesian model of disease progression in GNE myopathy. Stat Med. 2019 Apr 15;38(8):1459-74. doi: 10.1002/sim.8050
Neugebauer R, Schmittdiel JA, Zhu Z, Rassen JA, Seeger JD, Schneeweiss S. High-dimensional propensity score algorithm in comparative effectiveness research with time-varying interventions. Stat Med. 2015 Feb 28;34(5):753-81. doi: 10.1002/sim.6377
Garcia R, Benet M, Arnau C, Cobo E. Efficiency of the cross-over design: an empirical estimation. Stat Med. 2004 Dec 30;23(24):3773-80. doi: 10.1002/sim.2072.