This paper considers the problem of screening k multivariate normal populations (secondary data) with respect to a control population (primary data) in terms of covariance structure. A screening procedure, developed based upon statistical ranking and selection theory, is designed to include in the selected subset those populations which have the same (or similar) covariance structure as the control population, and exclude those populations which differ significantly. Formulas for computing the probability of a correct selection and the least favorable configuration are developed. The sample size required to achieve a specific probability requirement is also developed, with results presented in tabular form. This secondary data selection procedure is illustrated via an example with applications to radar signal processing. Academic PressAMS 1991 subject classifications: 62E15, 62F07, 62H10. Key words and phrases: hypergeometric function in matrix argument, indifference zone approach, eigenvalue, least favorable configuration, multivariate normal, probability of a correct screening, radar signal processing, ranking and selection, subset selection approach.

This article proposes a curtailed two-stage matched pairs design to shorten the drug development process in Phase II clinical trials for which there are two arms, a treatment arm and a control arm, and the primary goal being to test whether the treatment is significantly better than the control. The design presented in this article uses the inverse trinomial distribution to determine appropriate cutoff points for the termination or continuation of the trial at each stage and is best suited for trials in which there is a low success rate and the available sample size is limited, such as is the case for trials involving rare forms of cancer or other uncommon diseases.

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