multiple imputation
The rise of multiple imputation: a review of the reporting and implementation of the method in medical research
... Missing data are common in clinical and epidemiological studies [1-3], and may arise for a variety of reasons that are beyond the researcher’s control. Sometimes an indi- vidual refuses to participate in a wave of data ...
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A comparison of two approaches to implementing propensity score methods following multiple imputation
... is multiple imputation (MI), in which missing data are filled in with random draws from their predictive distributions based on the observed data, thereby producing multiple plausible ...
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Missing Data Methodology: Sensitivity analysis after multiple imputation
... Multiple imputation under the assumption of missing at random is a popular tool for the anal- ysis of partially observed data; however the MAR assumption is a strong untestable ...of multiple impu- ...
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Efficiency of multiple imputation to test for association in the presence of missing data
... a multiple imputation approach to deal with missing data on case-parent trios and evaluated the performance of this approach on the same simulated ...our multiple imputation approach has high ...
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Multiple imputation: an alternative to top coding for statistical disclosure control
... multiple imputation. We show in simulation studies that the multiple-imputation methods provide better inferences of the publicly released data than top coding, using straightforward ...
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Statistical Analysis Using Machine Learning Approach for Multiple Imputation of Missing Data
... of imputation method. Also introduces mean imputation [2] method to find out missing ...mean imputation are sample size is overestimated, variance is underestimated, correlation is negatively ...of ...
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Multiple Imputation of Missing Data: A Simulation Study on a Binary Response
... that multiple imputations generally perform particu- larly well ...While multiple imputations were ori- ginnally developed for larger datasets with small propor- tions of missing data, ...using ...
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A comparison of multiple imputation methods for missing data in longitudinal studies
... simplistic imputation models with few ...the multiple imputation method may use high-dimensional data with a large number of predictors, in such situations JM-MVN and FCS-Standard may incur ...
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Multiple imputation of missing composite outcomes in longitudinal data
... the imputation approaches described in ...the multiple imputation methods on data that resemble closely that which would be obtained in a real longitudinal ...
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Multiple imputation using chained equations for missing data in TIMSS: a case study
... (i.e., multiple imputation by chained ...multivariate imputation (White et ...of imputation models, given that there is one model for each variable with missing ...
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Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariates.
... multilevel multiple imputation approach described by [8] and imple- mented in [9], by incorporating the idea of random covariances ...allows imputation of variables that are completely missing in a ...
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An application of a pattern-mixture model with multiple imputation for the analysis of longitudinal trials with protocol deviations
... [11]. Yuan and Little [12] proposed mixed-effect hybrid models (MEHMs) framework, where the joint distribution of the measurement process and dropout process is fac- torized into the marginal distribution of random ...
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Missing data and multiple imputation in clinical epidemiological research
... In this example, we evaluated the performances of complete- case analysis and multiple imputation and presented results in Table 4. This example, which resembles the association between the risk of blood ...
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Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation
... single imputation often provide inefficient or invalid results, and model-based strategies require often sophisticated problem-specific implementation, multiple imputation (MI) is becoming ...
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Factors associated with low birth weight in Nepal using multiple imputation
... deck imputation, cold deck imputation and regression imputation come under single imputation in which missing values are replaced by synthetic values [2, ...single imputation assume ...
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Bootstrap and multiple imputation under missing data in AR(1) models
... the imputation phase, several copies of the data set are created each containing different imputed ...correct imputation model is very important for the performance of multiple ...
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Multiple imputation for handling missing outcome data when estimating the relative risk
... data, multiple imputation (MI) [9] has been widely adopted due to its flexibility and availability in statistical software packages ...uncertainty, multiple values are imputed for each missing ...
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Multiple Imputation for Missing Data in Repeated Measurements Using MCMC and Copulas
... using imputation methods to impute the best estimate of a missing value of the variable [7], ...[12]. Imputation methods base on three types of missingness as follows: 1) missing completely at random (MCAR, ...
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The effect of institutional quality on national wealth: An examination using multiple imputation method
... of imputation methods have been developed in this field, the special nature of institutions and the time trend effect make it difficult to develop an appropriate selection ...a multiple imputation ...
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Tuning multiple imputation by predictive mean matching and local residual draws
... Background: Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at ...settings. Imputation by predictive mean ...
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