Results 171 to 180 of about 293,301 (286)
Regulatory Experiences with the Use of Multiple Imputation for Missing Data in a Phase 3 Confirmatory Trial. [PDF]
Sassi-Sayadi M, Verweij P, Cornelisse P.
europepmc +1 more source
Alcohol relapse after liver transplantation is difficult to predict using abstinence duration alone. We developed a multifactor model integrating abstinence duration, psychosocial risk (SIPAT), and socioeconomic context (AUC 0.70). This approach may support individualized risk assessment and tailored follow‐up intensity; external validation is needed ...
Ayato Obana +9 more
wiley +1 more source
Multiple imputation for missing values in ordinal variables from cancer registry data when performing Cox proportional hazards regression. [PDF]
Kästner A +4 more
europepmc +1 more source
Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males +8 more
wiley +1 more source
A New Multiple Imputation Method for High-Dimensional Neuroimaging Data. [PDF]
Lu T +5 more
europepmc +1 more source
Abstract A multipore, multiphase, continuum model is assembled for the first time for room temperature sodium–sulfur (RT Na–S) batteries, with Na+ ion transport and redox reactions in the liquid electrolyte phase and semisolid phase of precipitates softened by the electrolyte solvent, as guided by molecular dynamics simulations in this study ...
Hakeem A. Adeoye +3 more
wiley +1 more source
Two-stage multiple imputation with a longitudinal composite variable. [PDF]
Wang X, Larson MG, Liu C.
europepmc +1 more source
The Challenge of Handling Structured Missingness in Integrated Data Sources
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson +6 more
wiley +1 more source
Reference-Based Multiple Imputation for Longitudinal Binary Data. [PDF]
Cro S +3 more
europepmc +1 more source
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah +3 more
wiley +1 more source

