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Latent class modelling is one method used in the evaluation of diagnostic tests when there is no gold standard test that is perfectly accurate. The technique demonstrates maximum likelihood estimates of the prevalence of a disease or a condition and the error rates of diagnostic tests or observers. This study reports the effect of departures from the latent class model assumption of independent misclassifications between observers or tests conditional on the true state of the individual being tested. It is found that estimates become biased in the presence of dependence. Most commonly the prevalence of the disease is overestimated when the true prevalence is at less than 50% and the error rates of dependent observers are underestimated. If there are also independent observers in the group, their error rates are overestimated. The most dangerous scenario in which to use latent class methods int he evaluation of tests is when the true prevalence is low and the false positive rate is high. This is common to many screening situations. / Thesis / Master of Science (MS)

This thesis covers misclassification probabilities via an Edgeworth-type expansion of the maximum likelihood based discriminant function. When deriving misclassification errors, first the expectation and variance in the population are assumed to be known where the variance is the same across populations and thereafter we consider the case where those parameters are unknown. Cumulants of the discriminant function for discriminating between two multivariate normal populations are derived. Approximate probabilities of the misclassification errors are established via an Edgeworth-type expansion using a standard normal distribution.