Results 11 to 20 of about 1,729,359 (279)

Diagnosing and Handling Common Violations of Missing at Random [PDF]

Psychometrika, 2023
Ignorable likelihood (IL) approaches are often used to handle missing data when estimating a multivariate model, such as a structural equation model. In this case, the likelihood is based on all available data, and no model is specified for the missing data mechanism.
Feng Ji, Sophia Rabe-Hesketh, Anders Skrondal   +2 more
openaire   +6 more sources

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

Statistical Machine Learning Methods to Handle Missing PHQ-8 Score – Assuming Missing at Random [PDF]

BJPsych Open
Aims Missing data is a challenge that most researchers encounter. It is a concern that continues to be analyzed and addressed for solutions. Missing data occurs when there is no data stored for certain variables relating to participants.
Khalid Suliman, Mitha Al Balushi, Hannah Holliday, Manal Alblooshi, Amar Ahmad   +4 more
doaj   +2 more sources

Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity

BMC Medical Research Methodology, 2023
Background Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming ...
Jiaqi Tong, Fan Li, Michael O. Harhay, Guangyu Tong   +3 more
doaj   +1 more source

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

Lines missing every random point [PDF]

Computability, 2014
Abstract We prove that there is, in every direction in Euclidean space, a line that misses every computably random point. We also prove that there exist, in every direction in Euclidean space, arbitrarily long line segments missing every double exponential time random point.
Jack H. Lutz, Neil Lutz
openaire   +4 more sources

A simplified eye-opener on managing missing data and in evaluation of non-response bias in medical research: a narrative review

Journal of the College of Community Physicians, 2018
Using correct methods for prevention, analysis and treatment of missing data is essential in preserving the validity of scientific research. In spite of this, issues related to missing data and non-response bias are found to be inadequately discussed in ...
P. K. B. Mahesh, Wasantha Gunathunga, Mahendra arnold, S. B. Munasinghe, Sinha De Silva   +4 more
doaj   +1 more source

What impact do assumptions about missing data have on conclusions? A practical sensitivity analysis for a cancer survival registry

BMC Medical Research Methodology, 2017
Background Within epidemiological and clinical research, missing data are a common issue and often over looked in publications. When the issue of missing observations is addressed it is usually assumed that the missing data are ‘missing at random’ (MAR).
M. Smuk, J. R. Carpenter, T. P. Morris
doaj   +1 more source

Missing.... presumed at random: cost-analysis of incomplete data [PDF]

, 2002
When collecting patient-level resource use data for statistical analysis, for some patients and in some categories of resource use, the required count will not be observed. Although this problem must arise in most reported economic evaluations containing
Bang, Box, Brick, Buck, Carides, Coast, Diggle, Duan, Etzioni, Fenn, Gelfand, Greene, Hanson, Horton, Johnston, Keoghane, Leigh, Lin, Little, Little, Polsky, Roberts, Rubin, Rubin, Rubin, Schafer, Schafer, Tanner, van Buuren, Willan, WinBUGS, Wu   +31 more
core   +2 more sources

Comprehensive analysis of missing data imputation in clinical time-series: challenges, risks, and practical solutions [PDF]

PeerJ Computer Science
Missing data in clinical time series is pervasive and decision-critical, arising from irregular sampling, workflow-driven measurement policies, sensor failures, and intervention-dependent monitoring.
Aasim Ayaz Wani, Fatima Abeer
doaj   +2 more sources