Repeated measures studies are undertaken in all areas of Public Health. In repeated measures studies, the basic sampling unit is a group or cluster of subjects; a measurement is made on each subject within the cluster. Missing 'responses and covariates are common occurrences in repeated measures studies, and the majority of the proposal relates to missing data problems in repeated measures studies. First, we will evaluate and extend existing methods for estimating measures of association in longitudinal studies with missing responses. Secondly, the likelihood methods can be computationally intensive, so we propose a pseudolikelihood methods to estimate parameters of marginal models for longitudinal studies with nonignorable nonmonotone missing outcomes. Thirdly, conditional logistic regression is often used to eliminate nuisance 'fixed' cluster effects, and we propose a modified conditional logistic regression which is appropriate to use with missing covariates. Fourthly, because of current techniques of determining gene mutation, investigators are now interested in estimating the odds ratio between genetic status (mutation, no mutation) and treatment success yes, no). Unfortunately, it is not always possible to perform a complete genetic evaluation to determine if a gene has mutated, resulting in 'missing data'. We will apply missing data methods to analyze the mutation status of the gene. Our final proposed project does not concern missing data, although it does evaluate a method that can give biased results in clustered data studies. We will determine if clustering affects the usual test statistics of no treatment effect in a randomized clinical trial.
