Objective: To test the predictive ability of multinomial regression method in obtaining category of death distribution for cases with unknown/ill-defined mortality codes.
Methods: The authors evaluated the performance of the multinomial regression model by fitting the model to trial datasets from 2004 Mexican vital registration data. To predict category of death, the regression method makes use of explanatory variables, such as gender, age, place of crash, place of residence, education and insurance type. The authors compared the results of a full model regression with those of a reduced model that only contained gender and age as explanatory variables. For this comparison, the authors constructed two forms of data: dummy variable adjustment method and case-wise deleted method. The comparison was made through estimated area under the curve (AUC) for each outcome variable.
Results: The full model significantly outperformed the gender-age (reduced) model using both datasets. In the case-wise deleted method, the AUC was increased from 0.55 to 0.7 for the reduced model and from 0.64 to 0.84 for the full model. Improvement in AUC using the dummy variable adjustment method was less significant.
Conclusions: To predict ill-defined categories of death, adding relevant explanatory variables to gender and age is recommended. Multiple imputations may perform even better than this model especially when significant portion of the data are missing.