Analysis of Multi-State Models with Mismeasured Covariates or Misclassified States
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Date
2015-05-22
Authors
He, Feng
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
Multi-state models provide a useful framework for estimating the rate
of transitions between defined disease states and understanding the
influence of covariates on transitions in studies of the disease
progression. Statistical analysis of data from studies of disease
progression often involves a number of challenges. A particular
challenge is that the classification of the disease state may be
subject to error. Another common problem is that there are many
sources of heterogeneity in the data in which situation the assumption
of time-homogeneous for common Markov models is not valid. In
addition, it is common for discrete covariates subject to
misclassification and the panel data collected from disease
progression studies is time-dependence in the covariates.
In Chapter 2, the progressive multi-state model with misclassification
is developed to simultaneously estimate transition rates and account
for potential misclassification. The performance of the maximum
likelihood and pairwise likelihood estimators is evaluated by
simulation studies. The proposed progressive model is illustrated on
coronary allograft vasculopathy data, in which the diagnosis based on
the coronary angiography is subject to error.
In Chapter 3, hidden mover-stayer models are proposed to provide a
solution to a type of heterogeneity where the population consists of
both movers and stayers in the presence of misclassification. The
likelihood inference procedure based on the EM algorithm is developed
for the proposed model. The performance of the likelihood method is
investigated through simulation studies. The proposed method is
applied to the Waterloo Smoking Prevention Project.
In Chapter 4, we propose estimation procedures for Markov models with
binary covariates subject to misclassification. We show that the model
is not identifiable under covariate misclassification. Consequently,
we develop likelihood inference methods based on known
reclassification probabilities and the main/validation study
design. Simulation studies are conducted to investigate the
performance of proposed methods and the consequence of the naive
analysis which ignores the misclassification.
In Chapter 5, we consider two-state Markov models where time-dependent
surrogate covariates are available. We exploit both functional and
structural inference methods to reduce or remove bias effects induced
from covariate measurement error. The performance of proposed methods
is investigated based on simulation studies.
Description
Keywords
panel data, measurement error, multi-state models, misclassification