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 +6 more
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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 +3 more sources
Statistical Machine Learning Methods to Handle Missing PHQ-8 Score – Assuming Missing at Random [PDF]
BJPsych OpenAims 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 +4 more
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MISSING AT RANDOM AND IGNORABILITY FOR INFERENCES ABOUT SUBSETS OF PARAMETERS WITH MISSING DATA [PDF]
, 2013 For likelihood-based inferences from data with missing values, Rubin (1976) showed that the missing data mechanism can be ignored when (a) the missing data are missing at random (MAR), in the sense that missingness does not depend on the missing values ...
Roderick J. A. Little, Sahar Zanganeh
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COVARIATE DECOMPOSITION METHODS FOR LONGITUDINAL MISSING-AT-RANDOM DATA AND PREDICTORS ASSOCIATED WITH SUBJECT-SPECIFIC EFFECTS. [PDF]
Aust N Z J Stat, 2014 Neuhaus JM, McCulloch CE.
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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
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Bayesian Estimation of Log-Normal Distribution Under Ranked Set Sampling With Missing Data
IEEE Access, 2021 In this paper, joint Bayesian estimation of two parameters of a log-normal distribution is obtained based on simple random sampling (SRS) and ranked set sampling (RSS) with complete and missing data.
Fengxi Zong, Rubing Li
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Joint Models for Incomplete Longitudinal Data and Time-to-Event Data
Mathematics, 2022 Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data.
Yuriko Takeda +2 more
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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
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Impact on Cronbach's alpha of simple treatment methods for missing data [PDF]
Tutorials in Quantitative Methods for Psychology, 2016 The scientific treatment of missing data has been the subject of research for nearly a century. Strangely, interest in missing data is quite new in the fields of educational science and psychology (Peugh & Enders, 2004; Schafer & Graham, 2002). It is now
Béland, Sébastien +2 more
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