Multiple imputation: review of theory, implementation and software

Harel, Ofer; Zhou, Xiao‐Hua

doi:10.1002/sim.2787

Cited by 347 publications

(278 citation statements)

References 50 publications

(70 reference statements)

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“…Imputation was performed separately on the derivation cohort and the validation cohort, with the predictor variables chosen using the same strategy [40]. Each of the imputed sets was then analyzed as if it were complete, and the results pooled by the method presented by Rubin [41] to create inferences that validly reflects sampling variability as a result of imputation. The imputation method we used is well documented and accepted [40,41].…”

Section: Development Of Predictive Models Using Data Mining Analysismentioning

confidence: 99%

See 1 more Smart Citation

FibroGENE: A gene-based model for staging liver fibrosis

Eslam

Hashem

Romero–Gómez

et al. 2016

Journal of Hepatology

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: A gene-based model for staging liver fibrosis, Journal of Hepatology (2015), doi: http://dx.doi.org/10. 1016/j.jhep.2015.11.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Background& aim: The extent of liver fibrosis predicts long term outcomes, and hence impacts

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Section: Development Of Predictive Models Using Data Mining Analysismentioning

confidence: 99%

“…Each of the imputed sets was then analyzed as if it were complete, and the results pooled by the method presented by Rubin [41] to create inferences that validly reflects sampling variability as a result of imputation. The imputation method we used is well documented and accepted [40,41].…”

Section: Development Of Predictive Models Using Data Mining Analysismentioning

confidence: 99%

FibroGENE: A gene-based model for staging liver fibrosis

Eslam

Hashem

Romero–Gómez

et al. 2016

Journal of Hepatology

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“…Multiple imputation (MI) (method 11) was introduced, [13], [27], [40] to take into account the uncertainty that is caused by the existence of the missing data, by creating complete data sets [12], [15], [22], [27]. Usually, a small number of sets is adequate m=3-5.…”

Section: G Multipe Imputationmentioning

confidence: 99%

Dealing with missing data for prognostic purposes

Loukopoulos

Sampath

Pilidis

et al. 2016

2016 Prognostics and System Health Management Conference (PHM-Chengdu)

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Abstract-Centrifugal compressors are considered one of the most critical components in oil industry, making the minimisation of their downtime and the maximisation of their availability a major target. Maintenance is thought to be a key aspect towards achieving this goal, leading to various maintenance schemes being proposed over the years. Condition based maintenance and prognostics and health management (CBM/PHM), which is relying on the concepts of diagnostics and prognostics, has been gaining ground over the last years due to its ability of being able to plan the maintenance schedule in advance. The successful application of this policy is heavily dependent on the quality of data used and a major issue affecting it, is that of missing data. Missing data's presence may compromise the information contained within a set, thus having a significant effect on the conclusions that can be drawn from the data, as there might be bias or misleading results. Consequently, it is important to address this matter. A number of methodologies to recover the data, called imputation techniques, have been proposed. This paper reviews the most widely used techniques and presents a case study with the use of actual industrial centrifugal compressor data, in order to identify the most suitable ones.

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“…On the other hand likelihood-based approaches to doi: 10.15171/ijhpm.2013.11 impute missing data are available. Among them, Multiple Imputation via Chained Equations (MICE) is established as the standard tool (14,15). In MICE, each missing datum is replaced multiple times, therefore creating more than one (typically 10) data sets.…”

Section: Introductionmentioning

confidence: 99%

“…MICE is referred to as multiple imputation method, as it replaces each missing value with multiple plausible values (15).…”

Section: Introductionmentioning

confidence: 99%

Influence of Pattern of Missing Data on Performance of Imputation Methods: An Example Using National Data on Drug Injection in Prisons

Haji‐Maghsoudi

Haghdoost

Rastegari

et al. 2013

Int J Health Policy Manag

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Background:Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern, to be addressed here, is the role of the pattern of missing data. Methods: We used information of 2720 prisoners. Results derived from fitting regression model to whole data were served as gold standard. Missing data were then generated so that 10%, 20% and 50% of data were lost. In scenario 1, we generated missing values, at above rates, in one variable which was significant in gold model (age). In scenario 2, a small proportion of each of independent variable was dropped out. Four imputation methods, under different Event Per Variable (EPV) values, were compared in terms of selection of important variables and parameter estimation. Results: In scenario 2, bias in estimates was low and performances of all methods for handing missing data were similar. All methods at all missing rates were able to detect significance of age. In scenario 1, biases in estimations were increased, in particular at 50% missing rate. Here at EPVs of 10 and 5, imputation methods failed to capture effect of age. Conclusion:In scenario 2, all imputation methods at all missing rates, were able to detect age as being significant. This was not the case in scenario 1. Our results showed that performance of imputation methods depends on the pattern of missing data.

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Multiple imputation: review of theory, implementation and software

Cited by 347 publications

References 50 publications

FibroGENE: A gene-based model for staging liver fibrosis

FibroGENE: A gene-based model for staging liver fibrosis

Dealing with missing data for prognostic purposes

Influence of Pattern of Missing Data on Performance of Imputation Methods: An Example Using National Data on Drug Injection in Prisons

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