Comparing predictive values of two diagnostic tests
by Cho, Yoonjin, Ph.D., North Carolina State University, 2009, 77 pages; AAT 3395129
Abstract (Summary)
Positive and negative predictive values are important measures of accuracy when one compares medical diagnostic tests. When more than one diagnostic test are available, one may have to choose one of the possible diagnostic tests due to cost, time, or ethical reasons. We consider a paired study design in cohort study where two diagnostic tests are measured on every patient. Our parameter of interest is the log odds of predictive values. In first chapter, we review current methods for comparing diagnostic tests when gold standards are available on every individual. We propose method by series of logistic regressions and derive estimator and test statistics based on likelihood probability. 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 be biased. In Chapter 2 and 3, we extend the methods to when gold standard is missing. We assume that missing gold standard is missing at random, which means missing pattern only depends on observed data. In Chapter 2, we use semiparametric theory to derive a class of regular and asymptotically normal estimators of our parameter of interest. Out of the class, we derive an estimator which is the most efficient in the class by 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.
http://repository.lib.ncsu.edu/ir/handle/1840.16/5305?mode=full&submit_simple=Show+full+item+record
请国外的师兄帮忙,不胜感激!