DESCRIPTION (Adapted from the Applicant's Abstract): In studies of cancer and
AIDS, patients are monitored for clinical events and laboratory markers that
are known to be associated with declining health and an increased risk of
death. Understanding the pattern of disease progression is important for the
clinical management of individual patients, as well as for the design and
analysis of clinical trials for new therapies. The statistical analysis of
disease progression is complicated by the fact that patients miss visits,
resulting in incomplete information either on when an event occurred (interval
censored data) or on the value of a clinical or laboratory measurement at some
points in time. This issue is made more complex in the context of a study of a
fatal disease, where it is important to appropriately handle mortality. This
proposal is aimed at determining a useful approach to analyzing multiple
outcomes of progression from a study with missing, truncated, and censored
data.
For this, we have four aims. 1.) We will develop methods for the analysis of
multivariate failure time data, where the failures are subject to interval
censoring. 2.) We will develop a method for the analysis of the failure time
data from a cancer genetics study, where the participants are subject to
truncation because the genetic test became available only recently (1996). 3.)
We will develop a method for the analysis of screening data, for which the
chance of being screened is dependent on the event (failure) of interest
(informative censoring). 4.) We will develop methods of estimating the effect
of longitudinal changes in a marker of progression on the hazard of a
subsequent event indicating progression. These methods will be applied to data
from clinical trials and observational studies in cancer (conducted at the MGH
Cancer Center), in AIDS (conducted by the NIH sponsored AIDS Clinical Trials
Group), and in cancer genetics (conducted by the NIH sponsored Cancer Genetics
Network).
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