Please Note: This seminar will be given online.
Combining rules for F- and Beta-statistics from multiply-imputed data
Missing values in data impedes the task of inference for population parameters of interest. Multiple Imputation (MI) is a popular method for handling missing data since it accounts for the uncertainty of missing values. Inference in MI involves combining point and variance estimates from each imputed data via Rubin's combining rules. A sufficient condition for these rules is that the estimator is approximately (multivariate) normally distributed. However, these traditional combining rules get computationally cumbersome for multicomponent parameters of interest, and unreliable at high rate of missingness (due to an unstable variance matrix). This talk proposes combining rules for F- and Beta-statistics from multiply-imputed data for decisions about multicomponent parameters. These proposed combining rules have the advantage of being computationally convenient since they only involve univariate F- and Beta-statistics, while providing the same inferential reliability as the traditional multivariate combining rules. Our simulation study demonstrates that the proposed method has good statistical properties (of maintaining low type I and type II error rates at relatively large proportions of missingness). The general applicability of the proposed method is demonstrated within a lead exposure study to assess the association between lead exposure and neurological motor function.