Comparing Predictive Values of Two diagnostic tests
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Date
2009-07-29
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Abstract
Positive and negative predictive values are important measures of accuracy when
one compares the accuracy of diagnostic tests. When more than one diagnostic tests are
available, one may has to choose one of the possible diagnostic tests due to cost, time, or
ethical reason. We consider a pair study design on cohort study where two diagnostic tests
are measured on every patients. Our parameter of interest is the log odds of predictive values.
In first chapter, we review current methods on comparing diagnostic tests when gold
standards are available on every individual. We propose our method by series of logistic
regressions and derive estimator and test statistics based on likelihood method. However, it
is often the case that gold standard is not observed on every patient because it may be invasive.
If we only consider those who have observed gold standard, the estimator may not be
biased. In Chapter 2 and 3, we extend the cases when gold standard is missing. We assume
that missing gold standard is missing at random, which is to depend on observed data. In
Chapter 2, we use semiparametric theory to derive a class of regular and asymptotically
normal of our parameter of interest. Out of the class, we derive an estimator which is the
most effcient in the class in using the information from available auxiliary covariates which
may be associated with the outcome of gold standard. We also use auxiliary covariates in
modeling the probability of observing gold standard. In Chapter 3, through M-estimator,
we derive another consistent estimator through imputation method.
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Keywords
Diagnostic Tests, Semiparametric Theory, Missing Data, M-estimator
Citation
Degree
PhD
Discipline
Statistics
