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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 …