Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: A clinical example

To illustrate the effects of different methods for handling missing data—complete case analysis, missing-indicator method, single imputation of unconditional and conditional mean, and multiple imputation (MI)—in the context of multivariable diagnostic research aiming to identify potential predictors...

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Vydané v:Journal of clinical epidemiology Ročník 59; číslo 10; s. 1102 - 1109
Hlavní autori: van der Heijden, Geert J.M.G., T. Donders, A. Rogier, Stijnen, Theo, Moons, Karel G.M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Inc 01.10.2006
Elsevier Limited
Predmet:
Adult
Aged
Bias
Complete case analysis
Epidemiologic Methods
Epidemiology
Female
Humans
Indicator method
Male
Methods
Middle Aged
Missing data
Multiple imputation
Precision
Prediction models
Pulmonary Embolism - diagnosis
Pulmonary Embolism - etiology
Regression analysis
Research Design
Single imputation
Studies
Missing data
Complete case analysis
Indicator method
Precision
Bias
Multiple imputation
Single imputation
ISSN:0895-4356, 1878-5921
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Shrnutí:To illustrate the effects of different methods for handling missing data—complete case analysis, missing-indicator method, single imputation of unconditional and conditional mean, and multiple imputation (MI)—in the context of multivariable diagnostic research aiming to identify potential predictors (test results) that independently contribute to the prediction of disease presence or absence. We used data from 398 subjects from a prospective study on the diagnosis of pulmonary embolism. Various diagnostic predictors or tests had (varying percentages of) missing values. Per method of handling these missing values, we fitted a diagnostic prediction model using multivariable logistic regression analysis. The receiver operating characteristic curve area for all diagnostic models was above 0.75. The predictors in the final models based on the complete case analysis, and after using the missing-indicator method, were very different compared to the other models. The models based on MI did not differ much from the models derived after using single conditional and unconditional mean imputation. 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. MI methods are known to be superior to single imputation methods. For our example study, the single imputation methods performed equally well, but this was most likely because of the low overall number of missing values.
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ISSN:0895-4356
1878-5921
DOI:10.1016/j.jclinepi.2006.01.015