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Pooled sample testing is an effective strategy to reduce the cost of disease surveillance in human and animal medicine. Testing pooled samples commonly produces matched observations with dichotomous responses in medical and epidemiological research. Although standard approaches exist for one-to-one paired binary data analyses, there is not much work on one-to-two or one-to-N matched binary data in the current statistical literature. The existing Miettinen's test assumes that the multiple observations from the same matched set are mutually independent. In this paper, we propose exact and asymptotic tests for one-to-two matched binary data. Our methods are markedly different from the previous studies in that we do not rely on the mutual independence assumption. The emphasis on the interdependence of observations within a matched set is inherent and attractive in both human health and veterinary medicine. It can be applied to all kinds of diagnostic studies with a one-to-two matched data structure. Our methods can be generalized to the one-to-N matched case. We discuss applications of the proposed methods to the environmental testing of salmonella in the United States. [ABSTRACT FROM AUTHOR]

For rare disease drug development, the United States (US) Food and Drug Administration (FDA) has indicated that the same standards as those for drug products for common conditions will be applied. To assist the sponsors in rare disease drug development, the FDA has initiated several incentive programs to encourage the sponsors in rare disease drug development. In practice, these incentive programs may not help in achieving the study objectives due to the limited small patient population. To overcome this problem, some out-of-the-box innovative thinking and/or approaches, without jeopardizing the integrity, quality, and scientific validity of rare disease drug development, are necessarily considered. These innovative thinking and/or approaches include but are not limited to (i) sample size justification based on probability statements rather than conventional power analysis; (ii) demonstrating not-ineffectiveness and not-unsafeness rather than demonstrating effectiveness and safety with the small patient population (i.e., limited sample size) available; (iii) the use of complex innovative designs such as a two-stage seamless adaptive trial design and/or an n-of-1 trial design for flexibility and the efficient assessment of the test treatment under study; (iv) using real-world data (RWD) and real-world evidence (RWE) to support regulatory submission; and (v) conducting an individual benefit–risk assessment for a complete picture of the clinical performance of the test treatment under investigation. In this article, we provide a comprehensive summarization of this innovative thinking and these approaches for an efficient, accurate and reliable assessment of a test treatment used for treating patients with rare diseases under study. Statistical considerations including challenges and justifications are provided whenever possible. In addition, an innovative approach that combines innovative thinking and these approaches is proposed for regulatory consideration in rare disease drug development. [ABSTRACT FROM AUTHOR]