Results 61 to 70 of about 287,343 (334)
Imputation estimators for unnormalized models with missing data [PDF]
Several statistical models are given in the form of unnormalized densities, and calculation of the normalization constant is intractable. We propose estimation methods for such unnormalized models with missing data.
Kim, Jae Kwang +3 more
core +3 more sources
Inhibition of Classical and Alternative Complement Pathway by Ravulizumab and Eculizumab
ABSTRACT Objective To explore the feasibility of classical (CH50) and alternative (AH50) complement pathway activity as potential biomarkers for treatment guidance and monitoring during therapy with ravulizumab in patients with generalized myasthenia gravis (gMG) and compare these to therapeutic drug monitoring under eculizumab.
Lea Gerischer +14 more
wiley +1 more source
Changes in Immune‐Inflammation Status and Acute Ischemic Stroke Prognosis in Prospective Cohort
ABSTRACT Background Inflammation is a critical risk factor for poor outcomes in cerebral infarction. Prior studies focused primarily on baseline inflammation status, neglecting dynamic longitudinal changes. We try to investigate the association between immune‐inflammation status alterations and stroke prognosis, and evaluated three systemic biomarkers'
Songfang Chen +11 more
wiley +1 more source
In this study, we suggest an optimal imputation strategy for the elevated estimation of the population mean of the primary variable utilizing the known auxiliary parameters for the missing observations.
Subhash Kumar Yadav +2 more
doaj +1 more source
Imputation Using Training Labels and Classification via Label Imputation
Missing data is a common problem in practice, and various imputation methods have been developed to deal with missing data. However, even though in many cases, the labels are available in the training data, the common practice of imputation usually only ...
Thu Nguyen +4 more
doaj +1 more source
Multiple imputation for handling missing outcome data when estimating the relative risk
Background Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk.
Thomas R. Sullivan +3 more
doaj +1 more source
Background Multiple imputation is frequently used to address missing data when conducting statistical analyses. There is a paucity of research into the performance of multiple imputation when the prevalence of missing data is very high. Our objective was
Peter C. Austin, Stef van Buuren
doaj +1 more source
Use of Symptomatic Drug Treatment for Fatigue in Multiple Sclerosis and Patterns of Work Loss
ABSTRACT Objective To describe the use of central stimulants and amantadine for fatigue in MS and evaluate a potential association with reduced work loss in people with MS. Methods We conducted a nationwide, matched, register‐based cohort study in Sweden (2006 to 2023) using national registers with prospective data collection.
Simon Englund +3 more
wiley +1 more source
Association of Corticospinal Tract Asymmetry With Ambulatory Ability After Intracerebral Hemorrhage
ABSTRACT Background Ambulatory ability after intracerebral hemorrhage (ICH) is important to patients. We tested whether asymmetry between ipsi‐ and contra‐lesional corticospinal tracts (CSTs) assessed by diffusion tensor imaging (DTI) is associated with post‐ICH ambulation.
Yasmin N. Aziz +25 more
wiley +1 more source
Discovery and Targeted Proteomic Studies Reveal Striatal Markers Validated for Huntington's Disease
ABSTRACT Objective Clinical trials for Huntington's disease (HD) enrolling persons before clinical motor diagnosis (CMD) lack validated biomarkers. This study aimed to conduct an unbiased discovery analysis and a targeted examination of proteomic biomarkers scrutinized by clinical validation. Methods Cerebrospinal fluid was obtained from PREDICT‐HD and
Daniel Chelsky +8 more
wiley +1 more source

