Results 11 to 20 of about 1,716,816 (278)

What is the difference between missing completely at random and missing at random? [PDF]

Int J Epidemiol, 2014
The terminology describing missingness mechanisms is confusing. In particular the meaning of 'missing at random' is often misunderstood, leading researchers faced with missing data problems away from multiple imputation, a method with considerable ...
Bhaskaran K, Smeeth L.
europepmc   +7 more sources

Missing at random: a stochastic process perspective. [PDF]

Biometrika, 2022
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.
europepmc   +6 more sources

Block-Conditional Missing at Random Models for Missing Data [PDF]

Statistical Science, 2010
Two major ideas in the analysis of missing data are (a) the EM algorithm [Dempster, Laird and Rubin, J. Roy. Statist. Soc. Ser. B 39 (1977) 1--38] for maximum likelihood (ML) estimation, and (b) the formulation of models for the joint distribution of the
John D. Kalbfleisch, Roderick J. A. Little, Roderick J. A. Little Is, Yan Zhou   +3 more
core   +4 more sources

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.
Ji F, Rabe-Hesketh S, Skrondal A.
europepmc   +7 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

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

Health Economics, 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   +5 more sources

Missing at random, likelihood ignorability and model completeness

The Annals of Statistics, 2004
This paper provides further insight into the key concept of missing at random (MAR) in incomplete data analysis. Following the usual selection modelling approach we envisage two models with separable parameters: a model for the response of interest and a
Copas, John B., Lu, Guobing
core   +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   +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

Recursive partitioning for monotone missing at random longitudinal markers. [PDF]

Stat Med, 2013
The development of HIV resistance mutations reduces the efficacy of specific antiretroviral drugs used to treat HIV infection and cross‐resistance within classes of drugs is common. Recursive partitioning has been extensively used to identify resistance mutations associated with a reduced virologic response measured at a single time point; here we ...
Stock S, DeGruttola V.
europepmc   +4 more sources