Biometrics

Displaying 1 - 21 of 21
Guo, X., Yang, B., Loh, J. M., Wang, Q., & Wang, Y. (2024). A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data. Biometrics, 80(4). https://doi.org/10.1093/biomtc/ujae130
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Dai, R., Li, R., Lee, S., & Liu, Y. (2024). Controlling false discovery rate for mediator selection in high-dimensional data. Biometrics, 80(3). https://doi.org/10.1093/biomtc/ujae064
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Rudolph, K. E., Williams, N., & Díaz, I. (2023). Efficient and Flexible Estimation of Natural Direct and Indirect Effects under Intermediate Confounding and Monotonicity Constraints. Biometrics, 79(4), 3126–3139. https://doi.org/10.1111/biom.13850
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Renson, A., Hudgens, M. G., Keil, A. P., Zivich, P. N., & Aiello, A. E. (2023). Identifying and Estimating Effects of Sustained Interventions under Parallel Trends Assumptions. Biometrics, 79(4), 2998–3009. https://doi.org/10.1111/biom.13862
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Wang, Q., Loh, J. M., He, X., & Wang, Y. (2022). A Latent State Space Model for Estimating Brain Dynamics from Electroencephalogram (EEG) Data. Biometrics, 79(3), 2444–2457. https://doi.org/10.1111/biom.13742
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Xu, S., Wang, P., Fung, W. K., & Liu, Z. (2022). A Novel Penalized Inverse-Variance Weighted Estimator for Mendelian Randomization with Applications to COVID-19 Outcomes. Biometrics, 79(3), 2184–2195. https://doi.org/10.1111/biom.13732
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Tu, D., Goyal, M. S., Dworkin, J. D., Kampondeni, S., Vidal, L., Biondo-Savin, E., Juvvadi, S., Raghavan, P., Nicholas, J., Chetcuti, K., Clark, K., Robert-Fitzgerald, T., Satterthwaite, T. D., Yushkevich, P., Davatzikos, C., Erus, G., Tustison, N. J., Postels, D. G., Taylor, T. E., … Shinohara, R. T. (2022). Automated Analysis of Low-Field Brain MRI in Cerebral Malaria. Biometrics, 79(3), 2417–2429. https://doi.org/10.1111/biom.13708
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Liu, Z., Ye, T., Sun, B., Schooling, M., & Tchetgen Tchetgen, E. (2022). Mendelian Randomization Mixed-Scale Treatment Effect Robust Identification and Estimation for Causal Inference. Biometrics, 79(3), 2208–2219. https://doi.org/10.1111/biom.13735
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Comment, L., Coull, B. A., Zigler, C., & Valeri, L. (2021). Bayesian data fusion: Probabilistic sensitivity analysis for unmeasured confounding using informative priors based on secondary data. Biometrics, 78(2), 730–741. Portico. https://doi.org/10.1111/biom.13436
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Scharfstein, D. O., Steingrimsson, J., McDermott, A., Wang, C., Ray, S., Campbell, A., Nunes, E., & Matthews, A. (2021). Global sensitivity analysis of randomized trials with nonmonotone missing binary outcomes: Application to studies of substance use disorders. Biometrics, 78(2), 649–659. Portico. https://doi.org/10.1111/biom.13455
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Sheng, Y., Sun, Y., Huang, C., & Kim, M. (2021). Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach. Biometrics, 78(2), 679–690. Portico. https://doi.org/10.1111/biom.13429
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Xu, Y., Greene, T. H., Bress, A. P., Sauer, B. C., Bellows, B. K., Zhang, Y., Weintraub, W. S., Moran, A. E., & Shen, J. (2020). Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective. Biometrics, 78(1), 337–351. Portico. https://doi.org/10.1111/biom.13406
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