Usefulness of imputation for the analysis of incomplete otoneurologic data

The usefulness of imputation in the treatment of missing values of an otone urologic database for the discriminant analysis was evaluated on the basis of the agreement of imputed values and the analysis results. The data consi sted of six patient groups with vertigo (N = 564). There were 38 variables and 11% of the data was missing. Missing values were filled in with the mea ns, regression and Expectation-Maximisation (EM) imputation methods and a r andom imputation method provided the baseline results. Means, regression an d EM methods agreed on 41-42% of the imputed missing values. The level of a greement between these and the random method was 20-22%, Despite the modera te agreement between the means, regression and EM methods, the discriminant functions were similar and accurate (prediction accuracy 83-99%). The disc riminant functions obtained from the randomly imputed data were also accura te having prediction accuracy 88-97%. Imputation seems to be a useful metho d for treating the missing data in this database. However, a lot of data wa s missing in otoneurologic tests, which are likely to be of less importance in the diagnosis of vertiginous patients. Consequently, the disagreement o f the methods did not affect clearly the discriminant analysis, and, theref ore, future research requires more complete data and advanced imputation me thods. (C) 2000 Elsevier Science Ireland Ltd. All rights reserved.