Bias

Displaying 1 - 6 of 6CSV
Ellis, C. A., Oliver, K. L., Harris, R. V., Ottman, R., Scheffer, I. E., Mefford, H. C., Epstein, M. P., Berkovic, S. F., & Bahlo, M. (2024). Inflation of polygenic risk scores caused by sample overlap and relatedness: Examples of a major risk of bias. The American Journal of Human Genetics, 111(9), 1805–1809. https://doi.org/10.1016/j.ajhg.2024.07.014
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Kezios, K. L., Zimmerman, S. C., Buto, P. T., Rudolph, K. E., Calonico, S., Zeki Al Hazzouri, A., & Glymour, M. M. (2024). Overcoming Data Gaps in Life Course Epidemiology by Matching Across Cohorts. Epidemiology, 35(5), 610–617. https://doi.org/10.1097/ede.0000000000001761
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Scroggins, J. K., Hulchafo, I. I., Topaz, M., Cato, K., & Barcelona, V. (2024). Addressing bias in preterm birth research: The role of advanced imputation techniques for missing race and ethnicity in perinatal health data. Annals of Epidemiology, 94, 120–126. https://doi.org/10.1016/j.annepidem.2024.05.003
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Columbia Affiliation
Cerna-Turoff, I., Maurer, K., & Baiocchi, M. (2022). Pre-processing data to reduce biases: full matching incorporating an instrumental variable in population-based studies. International Journal of Epidemiology, 51(6), 1920–1930. https://doi.org/10.1093/ije/dyac097
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