A RANDOM-EFFECTS MODEL FOR MULTIPLE CHARACTERISTICS WITH POSSIBLY MISSING DATA

The use of random-effects models for the analysis of longitudinal data with missing responses has bean discussed by several authors. This ar ticle extends the random-effects model for a single characteristic to the case of multiple characteristics, allowing for arbitrary patterns of observed data. Two different structures for the covariance matrix o f measurement error are considered: uncorrelated error between respons es and correlation of error terms at the same measurement times. Param eters for this model are estimated via the EM algorithm. The set of eq uations for this estimation procedure is derived; these equations are appropriately modified to deal with missing data. The methodology is i llustrated with an example from clinical trials.