Treatment of missing values with imputation for the analysis of otologic data.
Usefulness of imputation in the treatment of missing values in an otologic database was studied. Missing values were filled in with means (ME), regression (LR) and Expectation-Maximization (EM) imputation methods. A random imputation method (RA) provided baseline results. ME, LR and EM methods agreed on 41-42% of the imputed missing values. The level of agreement between these and RA method was 20-22%. Despite the moderate agreement, discriminant functions were similar and accurate (prediction accuracy 83-99%) for each diagnosis. A lot of data were missing in otoneurotologic tests which have less weight in the diagnosis of vertiginous patients. Consequently, the disagreement of the methods did not affect discriminant analysis. Inputation seems to be a useful method to treat missing data in this database, but future research requires more complete data and advanced imputation methods.
Laurikkala
J
J
Department of Computer Science, University of Tampere, Finland. jpl@cs.uta.fi
Kentala
E
E
Juhola
M
M
Pyykkö
I
I
D003625:Data Collection / Q000706:statistics & numerical data; D016002:Discriminant Analysis; D004427:Ear Diseases / Q000175:diagnosis*; D006801:Humans; D016247:Information Storage and Retrieval / Q000706:statistics & numerical data; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D008575:Meniere Disease / Q000175:diagnosis / Q000209:etiology*; D009464:Neuroma, Acoustic / Q000175:diagnosis; D015203:Reproducibility of Results