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Background  Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin’s Rules (RR) are easily applied to pool parameter estimates. In a logistic regression model, to consider whether a categorical covariate with more than two levels significantly contributes to the model, different methods are available. For example pooling chi-square tests with multiple degrees of freedom, pooling likelihood ratio test statistics, and pooling based on the covariance matrix of the regression model. These methods are more complex than RR and are not available in all mainstream statistical software packages. In addition, they do not always obtain optimal power levels. We argue that the median of the  p -values from the overall significance tests from the analyses on the imputed datasets can be used as an alternative pooling rule for categorical variables. The aim of the current study is to compare different methods to test a categorical variable for significance after multiple imputation on applicability and power.    Methods  In a large simulation study, we demonstrated the control of the type I error and power levels of different pooling methods for categorical variables.    Results  This simulation study showed that for non-significant categorical covariates the type I error is controlled and the statistical power of the median pooling rule was at least equal to current multiple parameter tests. An empirical data example showed similar results.    Conclusions  It can therefore be concluded that using the median of the  p -values from the imputed data analyses is an attractive and easy to use alternative method for significance testing of categorical variables.    Electronic supplementary material  The online version of this article (doi:10.1186/s12874-017-0404-7) contains supplementary material, which is available to authorized users.

Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis