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Model-based standardization using multiple imputation
BackgroundWhen studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome...
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Multiple imputation methods for missing multilevel ordinal outcomes
BackgroundMultiple imputation (MI) is an established technique for handling missing data in observational studies. Joint modelling (JM) and fully...
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Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey
BackgroundIn many countries, the prevalence of non-communicable diseases risk factors is commonly assessed through self-reported information from...
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Missing Data in Patient-Reported Outcomes Research: Utilizing Multiple Imputation to Address an Unavoidable Problem
BackgroundPatient-reported outcomes (PROs) have become a focus in postoperative surgical care. Unfortunately, studies using PROs can be subject to...
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The effect of high prevalence of missing data on estimation of the coefficients of a logistic regression model when using multiple imputation
BackgroundMultiple imputation is frequently used to address missing data when conducting statistical analyses. There is a paucity of research into...
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A random item effects generalized partial credit model with a multiple imputation-based scoring procedure
PurposeRandom item effects item response theory (IRT) models have received much attention for more than a decade. However, more research is needed on...
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Should multiple imputation be stratified by exposure group when estimating causal effects via outcome regression in observational studies?
BackgroundDespite recent advances in causal inference methods, outcome regression remains the most widely used approach for estimating causal effects...
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The impact of different imputation methods on estimates and model performance: an example using a risk prediction model for premature mortality
ObjectiveTo compare how different imputation methods affect the estimates and performance of a prediction model for premature mortality.
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Studying missingness in spinal cord injury data: challenges and impact of data imputation
BackgroundIn the last decades, medical research fields studying rare conditions such as spinal cord injury (SCI) have made extensive efforts to...
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Comparing single and multiple imputation strategies for harmonizing substance use data across HIV-related cohort studies
BackgroundAlthough standardized measures to assess substance use are available, most studies use variations of these measures making it challenging...
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Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets
BackgroundMissing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the...
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Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse
BackgroundData loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical...
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On the use of multiple imputation to address data missing by design as well as unintended missing data in case-cohort studies with a binary endpoint
BackgroundCase-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset...
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Missing data imputation techniques for wireless continuous vital signs monitoring
Wireless vital signs sensors are increasingly used for remote patient monitoring, but data analysis is often challenged by missing data periods. This...
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The impact of imputation quality on machine learning classifiers for datasets with missing values
BackgroundClassifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in...
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Evaluation of multiple imputation approaches for handling missing covariate information in a case-cohort study with a binary outcome
BackgroundIn case-cohort studies a random subcohort is selected from the inception cohort and acts as the sample of controls for several outcome...
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A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis
BackgroundMissing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches...
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Using multiple imputation and intervention-based scenarios to project the mobility of older adults
BackgroundProjections of the development of mobility limitations of older adults are needed for evidence-based policy making. The aim of this study...
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Practical strategies for handling breakdown of multiple imputation procedures
Multiple imputation is a recommended method for handling incomplete data problems. One of the barriers to its successful use is the breakdown of the...
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What difference does multiple imputation make in longitudinal modeling of EQ-5D-5L data? Empirical analyses of simulated and observed missing data patterns
PurposeAlthough multiple imputation is the state-of-the-art method for managing missing data, mixed models without multiple imputation may be equally...
