Results 261 to 270 of about 49,636 (297)

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Methods for Inverse Probability of Attrition Weighting

, 2020
In this paper, we examine methods for Inverse Probability of Attrition Weighting (IPAW) in a cohort study. Such longitudinal studies often suffer from attrition bias when participants fail to attend follow up visits. IPAW is a common strategy to address attrition bias which allows for unbiased estimation of causal effects.
Ockerman, Franklin
openaire   +3 more sources

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Adjusted survival curves with inverse probability weights

Computer Methods and Programs in Biomedicine, 2004
Kaplan-Meier survival curves and the associated nonparametric log rank test statistic are methods of choice for unadjusted survival analyses, while the semiparametric Cox proportional hazards regression model is used ubiquitously as a method for covariate adjustment.
Stephen R. Cole, Miguel A. Hernán
openaire   +3 more sources

Missing Confounding Data in Marginal Structural Models: A Comparison of Inverse Probability Weighting and Multiple Imputation

International Journal of Biostatistics, 2008
Standard statistical analyses of observational data often exclude valuable information from individuals with incomplete measurements. This may lead to biased estimates of the treatment effect and loss of precision.
Erica E M Moodie, Joseph A C Delaney, Genevieve Lefebvre   +2 more
exaly   +2 more sources

Constructing inverse probability weights for institutional comparisons in healthcare

Statistics in Medicine, 2020
In comparing quality of care between hospitals, disease‐specific quality indicators measure structural, process, or outcome elements related to the care of a particular condition. Such comparisons can be framed in terms of causal contrasts, answering the question of whether a patient (or a population of patients on average) would receive different care
Thai‐Son Tang, Peter C. Austin, Keith A. Lawson, Antonio Finelli, Olli Saarela   +4 more
openaire   +3 more sources

Behind the Numbers: Inverse Probability Weighting

Radiology, 2014
Inverse probability weighting is a propensity score-based technique that can be used to compensate for imbalance in study groups. It is an alternative to regression-based adjustment of the outcomes. It has advantages over matching of cases on the basis of propensity scores when there are more than two groups to compare, when finding matches results in ...
openaire   +2 more sources

Review of inverse probability weighting for dealing with missing data

Statistical Methods in Medical Research, 2011
The simplest approach to dealing with missing data is to restrict the analysis to complete cases, i.e. individuals with no missing values. This can induce bias, however. Inverse probability weighting (IPW) is a commonly used method to correct this bias. It is also used to adjust for unequal sampling fractions in sample surveys. This article is a review
Shaun R, Seaman, Ian R, White
openaire   +2 more sources

Variable selection using inverse probability of censoring weighting

Statistical Methods in Medical Research, 2023
In this article, we propose two variable selection methods for adjusting the censoring information for survival times, such as the restricted mean survival time. To adjust for the influence of censoring, we consider an inverse probability of censoring weighted for subjects with events. We derive a least absolute shrinkage and selection operator (lasso)
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Adjusted win ratio using the inverse probability of treatment weighting

Journal of Biopharmaceutical Statistics, 2023
The win ratio method has been increasingly applied in the design and analysis of clinical trials. However, the win ratio method is a univariate approach that does not allow for adjusting for baseline imbalances in covariates, although a stratified win ratio can be calculated when the number of strata is small.
Duolao Wang, Sirui Zheng, Ying Cui, Nengjie He, Tao Chen, Bo Huang   +5 more
openaire   +2 more sources