Incomplete data is common in both observational studies and clinical

trials. Ignoring missing data may produce seriously biased

estimators and could lead to misleading results. During the last three

decades, a vast amount of work has been done in this area. The

approaches can be classified into the following main categories:

imputation methods, likelihood-based methods and inverse probability

weighting methods. Longitudinal and crossover studies with repeated

measures are particularly subject to missing observations. Various

methods, including generalized estimating equations (GEE) (Liang and

Zeger, 1986), weighted GEE (WGEE) (Robins, Rotnitzky and Zhao, 1995)

and multiple imputations, have been proposed to cope with missing

data in longitudinal studies. However, very few researchers have

explored the missing data issue in crossover studies. In addition to

reviewing and critiquing the methods dealing with missing

observations in general and in repeated measures, in this dissertation, we

propose a new weighting approach for GEE to estimate the regression

parameters in crossover studies. The proposed method provides

consistent and asymptotically normally distributed estimators.

Simulation and asymptotic efficiency results indicate that the proposed

estimators are more efficient than both regular GEE and WGEE.

Applications of the proposed method are illustrated with real

data.