Results 1 to 10 of about 549,387 (258)

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

Regularized approach for data missing not at random. [PDF]

Stat Methods Med Res, 2019
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.
Tseng CH, Chen YH.
europepmc   +4 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

Mediation analysis with the mediator and outcome missing not at random. [PDF]

J Am Stat Assoc
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 ...
Zuo S, Ghosh D, Ding P, Yang F.
europepmc   +3 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

Implementation of Instrumental Variable Bounds for Data Missing Not at Random. [PDF]

Epidemiology, 2018
Instrumental variables are routinely used to recover a consistent estimator of an exposure causal effect in the presence of unmeasured confounding. Instrumental variable approaches to account for nonignorable missing data also exist but are less familiar to epidemiologists. Like instrumental variables for exposure causal effects, instrumental variables
Marden JR, Wang L, Tchetgen EJT, Walter S, Glymour MM, Wirth KE.   +5 more
europepmc   +4 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

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

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

Sensitivity analyses for trials with missing data, assuming missing not at random mechanisms [PDF]

Trials, 2013
In randomised trials with missing data, it is not uncommon for the observation of the outcome to depend on the outcome itself. For example in behavioural trials on smoking cessation, weight loss, or alcohol reduction, unsuccessful participants may be less willing to disclose their outcome than those that are more successful. These Missing Not At Random
Leurent B, Crawford M, Gilbert H, Morris R, Sweeting M, Nazareth I.   +5 more
europepmc   +3 more sources

Model Selection with Missing Data Embedded in Missing-at-Random Data

Stats, 2023
When models are built with missing data, an information criterion is needed to select the best model among the various candidates. Using a conventional information criterion for missing data may lead to the selection of the wrong model when data are not ...
Keiji Takai, Kenichi Hayashi
doaj   +1 more source