Results 61 to 70 of about 1,714,069 (326)
Nonparametric Estimation of ROC Surfaces Under Verification Bias
Verification bias is a well known problem that can affect the statistical evaluation of the predictive ability of a diagnostic test when the true disease status is unknown for some of the patients under study.
Khanh To Duc +2 more
doaj +1 more source
Randomly and Non-Randomly Missing Renal Function Data in the Strong Heart Study: A Comparison of Imputation Methods. [PDF]
Kidney and cardiovascular disease are widespread among populations with high prevalence of diabetes, such as American Indians participating in the Strong Heart Study (SHS).
Nawar Shara +7 more
doaj +1 more source
Developmental Disorders in Children Recently Diagnosed With Cancer
ABSTRACT Neurocognitive deficits in adult survivors of childhood cancer are well established, but less is known about developmental disorders (DD) arising shortly after cancer diagnosis. Using 2016–2019 linked Ohio cancer registry and Medicaid data, we compared DD among 324 children with cancer and 606,913 cancer‐free controls.
Jamie Shoag +5 more
wiley +1 more source
ABSTRACT Background B‐acute lymphoblastic leukemia (B‐ALL) is the most common pediatric cancer, and while most children in high‐resource settings are cured, therapy carries risks for long‐term toxicities. Understanding parents’ concerns about these late effects is essential to guide anticipatory support and inform evolving therapeutic approaches ...
Kellee N. Parker +7 more
wiley +1 more source
ABSTRACT Introduction Cognitive impairment and exercise intolerance are common in dialysis patients. Cerebral perfusion and oxygenation play a major role in both cognitive function and exercise execution; HD session per se aggravates cerebral ischemia in this population. This study aimed to compare cerebral oxygenation and perfusion at rest and in mild
Marieta P. Theodorakopoulou +10 more
wiley +1 more source
Using item response theory as a methodology to impute categorical missing values
Most datasets suffer from partial or complete missing values, which has downstream limitations on the available models on which to test the data and on any statistical inferences that can be made from the data.
Adrienne Kline, Yuan Luo
doaj +1 more source
Generating Synthetic Missing Data: A Review by Missing Mechanism
The performance evaluation of imputation algorithms often involves the generation of missing values. Missing values can be inserted in only one feature (univariate configuration) or in several features (multivariate configuration) at different ...
Miriam Seoane Santos +5 more
doaj +1 more source
Additive hazards regression with censoring indicators missing at random [PDF]
AbstractIn this article, the authors consider a semiparametric additive hazards regression model for right‐censored data that allows some censoring indicators to be missing at random. They develop a class of estimating equations and use an inverse probability weighted approach to estimate the regression parameters.
Song, Xinyuan +3 more
openaire +3 more sources
Revealing the structure of land plant photosystem II: the journey from negative‐stain EM to cryo‐EM
Advances in cryo‐EM have revealed the detailed structure of Photosystem II, a key protein complex driving photosynthesis. This review traces the journey from early low‐resolution images to high‐resolution models, highlighting how these discoveries deepen our understanding of light harvesting and energy conversion in plants.
Roman Kouřil
wiley +1 more source
Role of survey response rates on valid inference: an application to HIV prevalence estimates
Background Nationally-representative surveys suggest that females have a higher prevalence of HIV than males in most African countries. Unfortunately, these results are made on the basis of surveys with non-ignorable missing data.
Miguel Marino, Marcello Pagano
doaj +1 more source

