Kenward-Roger Approximation for Linear Mixed Models with Missing Covariates

• Activity Number: 528
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: International Indian Statistical Association
• Author(s): Akshita Chawla* and Tapabrata Maiti and Samiran Sinha
• Companies: Michigan State University and Michigan State University and Texas A&M University
• Keywords: fixed effects ; Kenward-Roger test ; likelihood ratio ; linear mixed models ; missing covariates ; small sample

Abstract

Partially observed variables are common in scientific research. Ignoring the subjects with partial information may lead to biased and or inefficient estimators, and consequently any test based only on the completely observed subjects may inflate the error probabilities. Missing data issue has been extensively considered in the regression model, especially in the independently identically (IID) data setup. Relatively less attention has been paid for handling missing covariate data in the linear mixed effect model-- a dependent data scenario. In case of complete data, Kenward-Roger's F test is a well established method for testing of fixed effects in a linear mixed model. In this paper, we present a modified Kenward-Roger type test for testing fixed effects in a linear mixed model when the covariates are missing at random. In the proposed method, we attempt to reduce bias from three sources, the small sample bias, the bias due to missing values, and the bias due to estimation of variance components. The operating characteristics of the method is judged and compared to two existing approaches, listwise deletion and mean imputation, via simulation studies.