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Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature.
Wyss R, Yanover C, El-Hay T, Bennett D, Platt RW, Zullo AR, Sari G, Wen X, Ye Y, Yuan H, Gokhale M, Patorno E, Lin KJ. Wyss R, et al. Among authors: patorno e. Pharmacoepidemiol Drug Saf. 2022 Sep;31(9):932-943. doi: 10.1002/pds.5500. Epub 2022 Jul 5. Pharmacoepidemiol Drug Saf. 2022. PMID: 35729705 Free PMC article. Review.
Methods for using clinical laboratory test results as baseline confounders in multi-site observational database studies when missing data are expected.
Raebel MA, Shetterly S, Lu CY, Flory J, Gagne JJ, Harrell FE, Haynes K, Herrinton LJ, Patorno E, Popovic J, Selvan M, Shoaibi A, Wang X, Roy J. Raebel MA, et al. Among authors: patorno e. Pharmacoepidemiol Drug Saf. 2016 Jul;25(7):798-814. doi: 10.1002/pds.4015. Epub 2016 May 4. Pharmacoepidemiol Drug Saf. 2016. PMID: 27146273
Real-World Evidence: What It Is and What It Can Tell Us According to the International Society for Pharmacoepidemiology (ISPE) Comparative Effectiveness Research (CER) Special Interest Group (SIG).
Yuan H, Ali MS, Brouwer ES, Girman CJ, Guo JJ, Lund JL, Patorno E, Slaughter JL, Wen X, Bennett D; ISPE Comparative Effectiveness Research Special Interest Group. Yuan H, et al. Among authors: patorno e. Clin Pharmacol Ther. 2018 Aug;104(2):239-241. doi: 10.1002/cpt.1086. Epub 2018 May 7. Clin Pharmacol Ther. 2018. PMID: 29733448
182 results