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

ABSTRACT:

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 significa

SUBMITTER: Eekhout I 

PROVIDER: S-EPMC5568368 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature