Biostatistics

Displaying 1 - 12 of 12
Rudolph, K. E., Williams, N. T., & Diaz, I. (2024). Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders. Biostatistics. https://doi.org/10.1093/biostatistics/kxae012
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Wu, X., Weinberger, K. R., Wellenius, G. A., Dominici, F., & Braun, D. (2023). Assessing the causal effects of a stochastic intervention in time series data: are heat alerts effective in preventing deaths and hospitalizations? Biostatistics, 25(1), 57–79. https://doi.org/10.1093/biostatistics/kxad002
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Wang, Q., & Wang, Y. (2022). Multilayer Exponential Family Factor models for integrative analysis and learning disease progression. Biostatistics, 25(1), 203–219. https://doi.org/10.1093/biostatistics/kxac042
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Nethery, R. C., Katz-Christy, N., Kioumourtzoglou, M.-A., Parks, R. M., Schumacher, A., & Anderson, G. B. (2021). Integrated causal-predictive machine learning models for tropical cyclone epidemiology. Biostatistics, 24(2), 449–464. https://doi.org/10.1093/biostatistics/kxab047
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Rudolph, K. E., & Díaz, I. (2021). Efficiently transporting causal direct and indirect effects to new populations under intermediate confounding and with multiple mediators. Biostatistics, 23(3), 789–806. https://doi.org/10.1093/biostatistics/kxaa057
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Tansey, W., Li, K., Zhang, H., Linderman, S. W., Rabadan, R., Blei, D. M., & Wiggins, C. H. (2021). Dose–response modeling in high-throughput cancer drug screenings: an end-to-end approach. Biostatistics, 23(2), 643–665. https://doi.org/10.1093/biostatistics/kxaa047
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Devick, K. L., Valeri, L., Chen, J., Jara, A., Bind, M.-A., & Coull, B. A. (2020). The role of body mass index at diagnosis of colorectal cancer on Black–White disparities in survival: a density regression mediation approach. Biostatistics, 23(2), 449–466. https://doi.org/10.1093/biostatistics/kxaa034
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