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A. Rogier T. Donders
Researcher at Radboud University Nijmegen
Publications - 72
Citations - 7190
A. Rogier T. Donders is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Randomized controlled trial & Mindfulness-based cognitive therapy. The author has an hindex of 30, co-authored 68 publications receiving 6233 citations. Previous affiliations of A. Rogier T. Donders include Utrecht University & Oklahoma State University Center for Health Sciences.
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Journal ArticleDOI
Review: A gentle introduction to imputation of missing values
A. Rogier T. Donders,A. Rogier T. Donders,Geert J. M. G. van der Heijden,Theo Stijnen,Karel G.M. Moons +4 more
TL;DR: In this paper, the authors show that both single and multiple imputations of missing data almost always result in biased estimates, and they also explain and illustrate why two commonly used methods to handle missing data, i.e., overall mean imputation and the missing-indicator method, almost always yield biased estimates.
Journal ArticleDOI
Emergence of azole resistance in Aspergillus fumigatus and spread of a single resistance mechanism.
Eveline Snelders,Henrich A. L. van der Lee,Judith Kuijpers,Anthonius J. M. M. Rijs,János Varga,János Varga,Robert A. Samson,Emilia Mellado,A. Rogier T. Donders,Willem J. G. Melchers,Paul E. Verweij +10 more
TL;DR: The presence of a dominant resistance mechanism in clinical isolates suggests that isolates with this mechanism are spreading in the authors' environment, and might be more prevalent than currently acknowledged.
Journal ArticleDOI
Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: A clinical example
Geert J. M. G. van der Heijden,Geert J. M. G. van der Heijden,A. Rogier T. Donders,A. Rogier T. Donders,Theo Stijnen,Karel G.M. Moons +5 more
TL;DR: In multivariable diagnostic research complete case analysis and the use of the missing-indicator method should be avoided, even when data are missing completely at random.
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Missing covariate data in medical research: To impute is better than to ignore
Kristel J.M. Janssen,A. Rogier T. Donders,Frank E. Harrell,Yvonne Vergouwe,Qingxia Chen,Diederick E. Grobbee,Karel G.M. Moons +6 more
TL;DR: This study shows that simple methods to deal with missing data can lead to seriously misleading results, and advises to consider multiple imputation.
Journal ArticleDOI
Dealing With Missing Outcome Data in Randomized Trials and Observational Studies
TL;DR: Complete case analysis with covariate adjustment and multiple imputation yield similar estimates in the event of missing outcome data, as long as the same predictors of missingness are included, and can and should be used as the analysis of choice more often.