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When there is a lack of a gold standard, an algorithm of classifying subjects into the case and non\u2010case status is necessary for combining multiple markers. The aim of this paper is to develop a method to construct a composite test from multiple applicable tests and derive an optimal classification rule under the absence of a gold standard. Rather than combining the tests, we treat the tests as a sequence. This sequential composite test is based on a mixture of two multivariate normal latent models for the distribution of the test results in case and non\u2010case groups, and the optimal classification rule is derived returning the greatest sensitivity at a given specificity. This method is applied to a real\u2010data example and simulation studies have been carried out to assess the statistical properties and predictive accuracy of the proposed composite test. This method is also attainable to implement nonparametrically. 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