Results 11 to 20 of about 1,718,863 (277)

Sensitivity analyses for data missing at random versus missing not at random using latent growth modelling: a practical guide for randomised controlled trials [PDF]

BMC Medical Research Methodology, 2022
Background Missing data are ubiquitous in randomised controlled trials. Although sensitivity analyses for different missing data mechanisms (missing at random vs. missing not at random) are widely recommended, they are rarely conducted in practice.
Andreas Staudt, Jennis Freyer-Adam, Till Ittermann, Christian Meyer, Gallus Bischof, Ulrich John, Sophie Baumann   +6 more
doaj   +2 more sources

Positive-Unlabeled Learning in Implicit Feedback from Data Missing-Not-At-Random Perspective [PDF]

Entropy
The lack of explicit negative labels issue is a prevalent challenge in numerous domains, including CV, NLP, and Recommender Systems (RSs). To address this challenge, many negative sample completion methods are proposed, such as optimizing sample ...
Sichao Wang, Tianyu Xia, Lingxiao Yang
doaj   +2 more sources

Regularized approach for data missing not at random [PDF]

Statistical Methods in Medical Research, 2017
It is common in longitudinal studies that missing data occur due to subjects’ no response, missed visits, dropout, death or other reasons during the course of study. To perform valid analysis in this setting, data missing not at random (MNAR) have to be considered.
Chi-Hong, Tseng, Yi-Hau, Chen
openaire   +3 more sources

Data Missing Not at Random in Mobile Health Research: Assessment of the Problem and a Case for Sensitivity Analyses [PDF]

Journal of Medical Internet Research, 2021
BackgroundMissing data are common in mobile health (mHealth) research. There has been little systematic investigation of how missingness is handled statistically in mHealth randomized controlled trials (RCTs).
Simon B Goldberg, Daniel M Bolt, Richard J Davidson   +2 more
doaj   +2 more sources

Gradient-Based Multiple Robust Learning Calibration on Data Missing-Not-at-Random via Bi-Level Optimization [PDF]

Entropy
Recommendation systems (RS) have become integral to numerous digital platforms and applications, ranging from e-commerce to content streaming field. A critical problem in RS is that the ratings are missing not at random (MNAR), which is due to the users ...
Shuxia Gong, Chen Ma
doaj   +2 more sources

Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study [PDF]

BMC Medical Research Methodology
Background Bias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters that govern the ...
Emily Kawabata, Daniel Major-Smith, Gemma L. Clayton, Chin Yang Shapland, Tim P. Morris, Alice R. Carter, Alba Fernández-Sanlés, Maria Carolina Borges, Kate Tilling, Gareth J. Griffith, Louise A. C. Millard, George Davey Smith, Deborah A. Lawlor, Rachael A. Hughes   +13 more
doaj   +3 more sources

Mediation Analysis with the Mediator and Outcome Missing Not at Random [PDF]

Journal of the American Statistical Association
Mediation analysis is widely used for investigating direct and indirect causal pathways through which an effect arises. However, many mediation analysis studies are challenged by missingness in the mediator and outcome. In general, when the mediator and outcome are missing not at random, the direct and indirect effects are not identifiable without ...
Shuozhi Zuo, Debashis Ghosh, Peng Ding, Fan Yang   +3 more
openaire   +4 more sources

Filling the gap in food and nutrition security data: What imputation method is best for Africa's food and nutrition security?

Lithuanian Journal of Statistics, 2022
Our study presents the methods adopted to produce accurate imputed values for Africa's food security and nutrition (FSN). We focused primarily on the following five imputation methods for handling missing data: Mean Imputation; Multiple Imputed values ...
Adusei Bofa, Temesgen Zewotir
doaj   +1 more source

Missing at random: a stochastic process perspective [PDF]

Biometrika, 2021
SummaryWe offer a natural and extensible measure-theoretic treatment of missingness at random. Within the standard missing-data framework, we give a novel characterization of the observed data as a stopping-set sigma algebra. We demonstrate that the usual missingness-at-random conditions are equivalent to requiring particular stochastic processes to be
Farewell, DM, Daniel, RM, Seaman, SR
openaire   +4 more sources

Random forest missing data algorithms [PDF]

Statistical Analysis and Data Mining: The ASA Data Science Journal, 2017
Random forest (RF) missing data algorithms are an attractive approach for imputing missing data. They have the desirable properties of being able to handle mixed types of missing data, they are adaptive to interactions and nonlinearity, and they have the potential to scale to big data settings.
Fei Tang, Hemant Ishwaran
openaire   +4 more sources