Interval censoring arises frequently in life history data, as individuals are
often only observed at a sequence of assessment times. This leads to a
situation where we do not know when an event of interest occurs, only that it
occurred somewhere between two assessment times. Here, the focus will be on
methods of estimation for recurrent event data, current status data, and
multistate data, subject to interval censoring.
With recurrent event data, the focus is often on estimating the rate and mean
functions. Nonparametric estimates are readily available, but are not smooth.
Methods based on local likelihood and the assumption of a Poisson process are
developed to obtain smooth estimates of the rate and mean functions without
specifying a parametric form. Covariates and extra-Poisson variation are
accommodated by using a pseudo-profile local likelihood. The methods are
assessed by simulations and applied to a number of datasets, including data
from a psoriatic arthritis clinic.
Current status data is an extreme form of interval censoring that occurs when
each individual is observed at only one assessment time. If current status
data arise in clusters, this must be taken into account in order to obtain
valid conclusions. Copulas offer a convenient framework for modelling the
association separately from the margins. Estimating equations are developed
for estimating marginal parameters as well as association parameters.
Efficiency and robustness to the choice of copula are examined for first and
second order estimating equations. The methods are applied to data from an
orthopedic surgery study as well as data on joint damage in psoriatic
arthritis.
Multistate models can be used to characterize the progression of a disease as
individuals move through different states. Considerable attention is given
to a three-state model to characterize the development of a back condition
known as spondylitis in psoriatic arthritis, along with the associated
risk of mortality. Robust estimates of the state occupancy probabilities are
derived based on a difference in distribution functions of the entry times.
A five-state model which differentiates between left-side and right-side
spondylitis is also considered, which allows us to characterize what effect
spondylitis on one side of the body has on the development of
spondylitis on the other side. Covariate effects are considered through
multiplicative time homogeneous Markov models. The robust state occupancy
probabilities are also applied to data on CMV infection in patients with HIV.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/3868 |
| Date | January 2008 |
| Creators | Tolusso, David |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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