Results 81 to 90 of about 287,343 (334)
Compensating for Missing Data from Longitudinal Studies Using WinBUGS [PDF]
Missing data is a common problem in survey based research. There are many packages that compensate for missing data but few can easily compensate for missing longitudinal data.
Adrian G. Barnett +3 more
core +1 more source
Fairness in Missing Data Imputation
, 2021 Missing data are ubiquitous in the era of big data and, if inadequately handled, are known to lead to biased findings and have deleterious impact on data-driven decision makings. To mitigate its impact, many missing value imputation methods have been developed.
Zhang, Yiliang, Long, Qi
openaire +2 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective In multiple sclerosis, the optimal time for deploying a therapeutic intervention is before the central nervous system is damaged; given the success of trials treating the earliest stage of MS, the radiologically isolated syndrome, developing primary prevention strategies is an important next challenge.
Amy W. Laitinen +7 more
wiley +1 more source
Imputation of numerical data under linear edit restrictions [PDF]
, 2011 A common problem faced by statistical offices is that data may be missing from collected data sets. The typical way to overcome this problem is to impute the missing data. The problem of imputing missing data is complicated by the fact that statistical
Coutinho, Wieger +2 more
core +1 more source
Imputation of Missing Network Data: Some Simple Procedures [PDF]
, 2014 Analysis of social network data is often hampered by non-response and missingdata. Recent studies show the negative effects of missing actors and ties on thestructural properties of social networks. This means that the results of socialnetwork analyses can be severely biased if missing ties were ignored and onlycomplete cases were analyzed. To overcome
openaire +3 more sources
Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data [PDF]
, 2023 Linying Ji +6 more
openalex +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Onasemnogene abeparvovec (OA) is an AAV9‐based gene therapy for spinal muscular atrophy type I (SMA I). Real‐world outcomes show increased response variability compared to clinical trials, and follow‐up data beyond 12–18 months are limited.
Marika Pane +43 more
wiley +1 more source
Financial Distress and Its Determinants in Rheumatoid Arthritis
Arthritis Care &Research, EarlyView.Objective To quantify the degree of financial distress and identify its determinants in adults with rheumatoid arthritis (RA) given the frequent prolonged use of expensive disease‐modifying therapies. Methods We identified adults enrolled in the FORWARD databank with either RA or noninflammatory musculoskeletal disease (NIMSKD) completing the ...
Amber Brown Keebler +5 more
wiley +1 more source
Missing Data Imputation for Supervised Learning
Applied Artificial Intelligence, 2018 Missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information. This paper compares methods for imputing missing categorical data for supervised classification tasks.
Jason Poulos, Rafael Valle
doaj +1 more source
Electronic Physician, 2017 Background: Missing values in data are found in a large number of studies in the field of medical sciences, especially longitudinal ones, in which repeated measurements are taken from each person during the study.
Amin Golabpour +4 more
doaj +1 more source