... code for weighted TREE-QMC is available on Github: ( https://github.com/molloy-lab/TREE-QMC ) species trees summary methods quartets gene tree error homology error missing data Accepted Manuscript Improved robustness to gene tree incompleteness, estimation errors, and systematic homology errors...

... the estimators. We discuss various considerations relevant when choosing an estimation approach, including computational time, how to handle missing data, whether to include post intercurrent event data in the analysis, whether and how to adjust for additional time-varying confounders, and whether and how...

.... HDMI approaches may decrease bias in studies where confounder missingness depends on unobserved factors. Missing data Confounding EHR NLP Real-World Evidence ORIGINAL UNEDITED MANUSCRIPT High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language...

... Attribution License ( https://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. causal inference directed acyclic graphs missing data missing at random missing not at random multiple...

..., it can lead to nonignorable missing pseudo-outcomes in the earlier stages. We provide a detailed illustration of this unique missing data problem with DTRs caused by backward-induction-induced nonignorable missing pseudo-outcomes in Section  2.3 . In this research, we aim to address the issue...

... of bariatric surgery on microvascular outcomes among a cohort of severely obese patients with Type II diabetes mellitus (T2DM). Target trial emulation missing data selection bias electronic health records inverse probability weighting ORIGINAL UNEDITED MANUSCRIPT American Journal of Epidemiology Submitted...

... through extensive simulation studies. The extracted latent features from the Bridge model consistently excel in predicting patient survival across six cancer types in GENIE BPC data. cancer genomics data integration dimension reduction missing data precision oncology systematic biases NIH 10.13039...

.... There is a growing literature on missing data imputation for tensors. However, existing methods give a point estimate for missing values without capturing uncertainty. We propose a multiple imputation approach for tensors in a flexible Bayesian framework, that yields realistic simulated values for missing entries...

... interest lies in the estimation of and inference about the parameters in the marginal mean model with link function , where targets the marginal treatment effect. When there is no missing data, an estimator of can be obtained by solving the following estimating equation (Liang and Zeger...

... cited. Summary Missing data in multiple variables is a common issue. We investigate the applicability of the framework of graphical models for handling missing data to a complex longitudinal pharmacological study of children with HIV treated with an efavirenz-based regimen as part of the CHAPAS-3 trial...

...Olga Chernomor; Christiane Elgert; Arndt von Haeseler; Andrey Rzhetsky Table 1 Summary for biological datasets ID Species group Sp. Loci Missing data (%) Max locus coverage Sp. min coverage Stand size Total CPU time D1 Snails 62 1027 37 58 (94%) 0 1 2 s D2...

... haematopoietic system multiple Gaussian graphical models missing data University of Palermo 10.13039/501100004913 (FFR2022) and (FFR2023) Research Projects of National Relevance B53D23009480006 Research Projects of National Relevance C53D23002580006 Italian Ministry of University...

... estimation missing data paradata semiparametric efficiency National Key R&D Program of China 10.13039/501100012166 2022YFA1008100 National Natural Science Foundation of China 10.13039/501100001809 12292983 12071015 Ministry of Education, Singapore 10.13039/501100001459...

.... Especially, the recovery of aliased and consecutively missing data is incredibly challenging. To solve this problem, we propose a novel framework for seismic interpolation using a recurrent inference mechanism (SIRIM). Integrating the advantages of supervised and unsupervised learning paradigms, we build...

... the study domain, which is much larger than the change in mean temperature levels over this time window. We illustrate how these characteristics result in increased spatial coverage of the events that exceed critical temperatures. climate change extreme values heatwaves missing data nonstationarity...

... variable did not have a large influence on the magnitude of bias reduction. Under missing outcome mechanism 3 (outcome missingness caused by the exposure only; Figure 2G-I ), no bias was observed when the auxiliary missingness data did not depend on any other variable (Plot G). When the auxiliary...

... packages are freely available on the Comprehensive R Archive Network (CRAN). Their source code is also available on GitHub ( https://github.com/bips-hb/micd ; https://github.com/bips-hb/tpc ). Causal discovery R cohort studies missing data longitudinal data National Institutes of Health 10.13039...

... available responses, but potentially overlook missing data intricacies. Advanced methods like multiple imputation (MI) address broader missing data, but demand increased computational resources. Researchers frequently use survey data in the All of Us Research Program (All of Us...

... for recognizing the benign and malignant cases. However, it can be a disadvantage as well, if the input data is not pre-processed properly before training the machine learning algorithms. Figure 4 presents the percentages of the missingness in the data. Figure 4 Missingness data percentages, a) from low, b...

... biomarkers derived from structural MRI. In this study, we adapted the Subtype and Stage Inference algorithm to handle missing data, enabling the application of Subtype and Stage Inference to multimodal data (magnetic resonance imaging, positron emission tomography, cerebrospinal fluid and cognitive tests...