Searched for: subject%3A%22Imputation%22
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Cai, M. (author), van Buuren, S. (author), Vink, G. (author)
In most medical research, the average treatment effect is used to evaluate a treatment's performance. However, precision medicine requires knowledge of individual treatment effects: What is the difference between a unit's measurement under treatment and control conditions? In most treatment effect studies, such answers are not possible because...
article 2022
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Fopma, A. (author), Cai, M. (author), van Buuren, S. (author), Vink, G. (author)
Curve matching is a prediction technique that relies on predictive mean matching, which matches donors that are most similar to a target based on the predictive distance. Even though this approach leads to high prediction accuracy, the predictive distance may make matches look unconvincing, as the profiles of the matched donors can substantially...
article 2022
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Kavelaars, X.M. (author), van Buuren, S. (author), van Ginkel, J.R. (author)
Multiple imputation is a highly recommended technique to deal with missing data, but the application to longitudinal datasets can be done in multiple ways. When a new wave of longitudinal data arrives, we can treat the combined data of multiple waves as a new missing data problem and overwrite existing imputations with new values (re-imputation)...
article 2022
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Cai, M. (author), van Buuren, S. (author), Vink, G. (author)
Missing data are often dealt with multiple imputation. A crucial part of the multiple imputation process is selecting sensible models to generate plausible values for incomplete data. A method based on posterior predictive checking is proposed to diagnose imputation models based on posterior predictive checking. To assess the congeniality of...
article 2022
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Austin, P.C. (author), van Buuren, S. (author)
Background: Multiple imputation is frequently used to address missing data when conducting statistical analyses. There is a paucity of research into the performance of multiple imputation when the prevalence of missing data is very high. Our objective was to assess the performance of multiple imputation when estimating a logistic regression...
article 2022
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Piedvache, A. (author), van Buuren, S. (author), Barros, H. (author), Ribeiro, A.I. (author), Draper, E. (author), Zeitlin, J. (author), EPICE Research group, (author)
Background: Loss to follow-up is a major challenge for very preterm (VPT) cohorts; attrition is associated with social disadvantage and parents with impaired children may participate less in research. We investigated the impact of loss to follow-up on the estimated prevalence of neurodevelopmental impairment in a VPT cohort using different...
article 2021
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Kavelaars, X.M. (author), van Ginkel, J.R. (author), van Buuren, S. (author)
Multiple imputation is a recommended technique to deal with missing data. We study the problem where the investigator has already created imputations before the arrival of the next wave of data. The newly arriving data contain missing values that need to be imputed. The standard method (RE-IMPUTE) is to combine the new and old data before...
article 2021
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Austin, P.C. (author), White, I.R. (author), Lee, D.S. (author), van Buuren, S. (author)
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the variables of interest are not measured or recorded for all subjects in the sample. Common approaches to addressing the presence of missing data include complete-case analyses, in which subjects with missing data are excluded, or mean-value...
article 2021
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Gorter, R. (author), Fox, J.P. (author), Eekhout, I. (author), Heymans, M.W. (author), Twisk, J. (author)
In medical research, repeated questionnaire data is often used to measure and model latent variables across time. Through a novel imputation method, a direct comparison is made between latent growth analysis under classical test theory and item response theory, while also including effects of missing item responses. For classical test theory and...
article 2020
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Brand, J.P.L. (author), van Buuren, S. (author), le Cessie, S. (author), van den Hout, W.B. (author)
In healthcare cost-effectiveness analysis, probability distributions are typically skewed and missing data are frequent. Bootstrap and multiple imputation are well-established resampling methods for handling skewed and missing data. However, it is not clear how these techniques should be combined. This paper addresses combining multiple...
article 2019
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Pereira Barata, A. (author), Takes, F.W. (author), van den Herik, H.J. (author), Veenman, C.J. (author)
conference paper 2019
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Eekhout, I. (author), de Vet, H.C. (author), de Boer, M.R. (author), Twisk, J.W. (author), Heymans, M.W. (author)
Previous studies showed that missing data in multi-item scales can best be handled by multiple imputation of item scores. However, when many scales are used, the number of items will become too large for the imputation model to reliably estimate imputations. A solution is to use passive imputation or a parcel summary score that combine and...
article 2018
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Audigier, V. (author), White, I.R. (author), Jolani, S. (author), Debray, T.P.A. (author), Quartagno, M. (author), Carpenter, J. (author), van Buuren, S. (author), Resche-Rigon, M. (author)
We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these...
article 2018
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Eekhout, I. (author), van de Wiel, M.A. (author), Heymans, M.W. (author)
Background. Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin’s Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels significantly contributes to the model,...
article 2017
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Audigier, V. (author), White, I.R. (author), Jolani, S. (author), Debray, T.P.A. (author), Quartagno, M. (author), Carpenter, J. (author), van Buuren, S. (author), Resche-Rigon, (author)
We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. Simulations are reproducible. The...
article 2017
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de Jong, R. (author), van Buuren, S. (author), Spiess, M. (author)
The sensitivity of multiple imputation methods to deviations from their distributional assumptions is investigated using simulations, where the parameters of scientific interest are the coefficients of a linear regression model, and values in predictor variables are missing at random. The performance of a newly proposed imputation method based...
article 2016
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Jolani, S. (author), Debray, T.P.A. (author), Koffijberg, H. (author), van Buuren, S. (author), Moons, K.G.M. (author)
Individual participant data meta-analyses (IPD-MA) are increasingly used for developing and validating multivariable (diagnostic or prognostic) risk prediction models. Unfortunately, some predictors or even outcomes may not have been measured in each study and are thus systematically missing in some individual studies of the IPD-MA. As a...
article 2015
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Vink, G. (author), Frank, L.E. (author), Pannekoek, J. (author), van Buuren, S. (author)
Multiple imputation methods properly account for the uncertainty of missing data. One of those methods for creating multiple imputations is predictive mean matching (PMM), a general purpose method. Little is known about the performance of PMM in imputing non-normal semicontinuous data (skewed data with a point mass at a certain value and...
article 2014
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Jolani, S. (author), van Buuren, S. (author)
Estimation in binary longitudinal data by using generalized estimating equation (GEE) becomes complicated in the presence of missing data because standard GEEs are only valid under the restrictive missing completely at random assumption. Weighted GEE has therefore been proposed to allow the validity of GEE's under the weaker missing at random...
article 2014
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Vink, G. (author), van Buuren, S. (author)
Current pooling rules for multiply imputed data assume infinite populations. In some situations this assumption is not feasible as every unit in the population has been observed, potentially leading to over-covered population estimates. We simplify the existing pooling rules for situations where the sampling variance is not of interest. We...
article 2014
Searched for: subject%3A%22Imputation%22
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