In many clinical and epidemiological studies one of the objectives is
to evaluate changes in the response variable over time. In such studies
subjects are followed, and their responses are measured at several time
points during the study period. It is common in these studies to lose
subjects due to death, withdrawal and loss to follow-up. Further
attrition may be related to the response, causing the data to be
informatively censored. For example, withdrawals may occur at greater
frequency among those who do not respond to the treatment than those
who do. Informative right censoring poses a challenge to statisticians
since many of the popularly used statistical methods assume that the
attrition probability is independent of the response variable.
The proposed project will focus on the analysis of longitudinal data
when the censoring process is considered informative and will develop
statistical methods for both continuous and discrete longitudinal data.
Application to cancer studies will be emphasized, and the developed
methods will be illustrated using actual data from clinical and
epidemiological studies of cancer.
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