[PDF] Top 20 Multiple Imputation of Missing Data: A Simulation Study on a Binary Response
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Multiple Imputation of Missing Data: A Simulation Study on a Binary Response
... caveats, multiple imputations by chained equations can be recommended for the analysis of data with missing ...of missing data is moderate—say up to 20%—and only main effects are of ... See full document
9
Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study
... However, simulation studies by Kalaycioglu et ...the imputation model, as implemented in our ...of multiple longitudinal variables with missing data, many of which were not ...the ... See full document
14
Dealing with missing data in the Center for Epidemiologic Studies Depression self-report scale: a study based on the French E3N cohort
... of missing data on the accuracy of estimates. The present study investigated MVs in the CES-D scale, a validated and easy-to-use tool, commonly used to iden- tify ...qualitative study showed ... See full document
11
Estimating range of influence in case of missing spatial data: a simulation study on binary data
... This simulation study investigated how the estimated range of influence was affected by missing outcomes in binary spatial ...ing data are a common feature in many ...the missing ... See full document
13
Outcome-sensitive multiple imputation: a simulation study
... to imputation are still routinely used [4], multiple imputation has been ac- cepted by methodologists as the most appropriate framework for dealing with MCAR and MAR mecha- nisms ...[5]. ... See full document
13
A comparison of multiple imputation methods for missing data in longitudinal studies
... our simulation study was based on a real dataset and thus has a realistic level of complexity, it is always difficult to draw general recommenda- tions from a single simulation ...when data ... See full document
16
A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study
... Background: Missing data is a common problem in epidemiological studies, and is particularly prominent in longitudinal data, which involve multiple waves of data ...Traditional ... See full document
11
Multiple imputation for handling missing outcome data when estimating the relative risk
... the missing data mechanism is unknown) and in simu- lation scenarios with different covariate characteristics, outcome prevalences and missing data mechanisms would certainly be ...current ... See full document
10
Imputation strategies for missing binary outcomes in cluster randomized trials
... categories: missing completely at random (MCAR), missing at random (MAR), covariate dependent (CD) missing, and missing not at random (MNAR) ...the missing data mechanism, i.e. ... See full document
15
Analysing mark recapture recovery data in the presence of missing covariate data via multiple imputation
... the missing-at-random assumption is a reasonable ...of missing covariate values for the younger age group(s), and in particular individuals in the study who do not have any observed covariate ... See full document
19
Semiparametric Methods for Decision Making and Causal Effect Generalization.
... the data is fully observed, which rarely occurs in ...available data is to use multiple imputation and pool estimators across imputed (complete) ...are multiple stages of ...complete ... See full document
139
A nonparametric multiple imputation approach for missing categorical data
... nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each ...for imputation is formed by measuring distances ... See full document
12
Methodological challenges in building composite indexes: Linking theory to practice
... using data provided by the Food and Agricultural ...aggregate data in order to compute composite indexes are compared and the relevancy of the choices to be made will be ...indicators, data ... See full document
11
Missing data imputation based on probabilistic data
... probabilistic imputation method had to be qualified by an evaluation framework (RQ ...A simulation technique was developed that tried out mean imputation, MICE and the probabilistic imputation ... See full document
8
Missing Categorical Data Imputation and Individual Observation Level Imputation
... the missing values (Y) follows for a given profi le the multinomial ...complete data years (t ≤ c) as the methodology for fi nding the determinants (X) of the distribution (structure) of Y (as the ... See full document
8
Multiple imputation using chained equations for missing data in TIMSS: a case study
... to multiple imputation—the multivariate nor- mal model and the chained equations ...approach. Multiple imputation is becoming easier and more tempting to use thanks to the existence of ... See full document
33
Multiple Imputation for Missing Data in Repeated Measurements Using MCMC and Copulas
... The data on the average rain volume in August 2009 were assigned missing at random with missing rate of 5%, 10%, 20% and 30%, ...rainfall data which included missing values, checked for ... See full document
5
Multiple Imputation for Missing Data Using Factored Regression Modelwith the Implementation of Current Population
... regression multiple imputation (SRMI) (also referred to as the multiple imputation by chained ...multivariate data are characterized by separate conditional models for each incomplete ... See full document
7
A Comparative Study between Multiple Imputation Method and Regression Imputation Method of Estimation of Missing Data
... For the regression example, two components to the uncertainty in the imputation process can be distinguished. The first component is the mean squared error from the regression which represents the between ... See full document
10
Missing data and multiple imputation in clinical epidemiological research
... Abstract: Missing data are ubiquitous in clinical epidemiological ...with missing data may differ from those with no missing data in terms of the outcome of interest and ... See full document
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