In this paper, we discuss statistical inference for a 2 × 2 table under inverse sampling, where the total number of cases is fixed by design. We demonstrate that the exact unconditional distributions of some relevant statistics differ from the distributions under conventional sampling, where the sample size is fixed by design. This permits us to define a simple unconditional alternative to Fisher's exact test. We provide an asymptotic argument including simulations to demonstrate that there is little power loss associated with the alternative test when the expected event rates are very small. We then apply the method to design a clinical trial in cataract surgery, where a rare side effect occurs in one in 1000 patients. The objective of the trial is to demonstrate that adjuvant treatment with an antibiotic will reduce this risk to one in 2000. We use an inverse sampling design and demonstrate how to set this up in a sequential manner. Particularly simple stopping rules can be defined when using the unconditional alternative to Fisher's exact test. Copyright © 2015 John Wiley & Sons, Ltd.

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Exact and asymptotic inference in clinical trials with small event rates under inverse sampling

Because pools are tested instead of individuals in group testing, this technique is helpful for estimating prevalence in a population or for classifying a large number of individuals into two groups at a low cost. For this reason, group testing is a well-known means of saving costs and producing precise estimates. In this paper, we developed a mixed-effect group testing regression that is useful when the data-collecting process is performed using inverse sampling. This model allows including covariate information at the individual level to incorporate heterogeneity among individuals and identify which covariates are associated with positive individuals. We present an approach to fit this model using maximum likelihood and we performed a simulation study to evaluate the quality of the estimates. Based on the simulation study, we found that the proposed regression method for inverse sampling with group testing produces parameter estimates with low bias when the pre-specified number of positive pools (r) to stop the sampling process is at least 10 and the number of clusters in the sample is also at least 10. We performed an application with real data and we provide an NLMIXED code that researchers can use to implement this method.

Inverse sampling regression for pooled data.

In a two-sample testing problem, sometimes one of the sample observations are difficult and/or costlier to collect compared to the other one. Also, it may be the situation that sample observations from one of the populations have been previously collected and for operational advantages we do not wish to collect any more observations from the second population that are necessary for reaching a decision. Partially sequential technique is found to be very useful in such situations. The technique gained its popularity in statistics literature due to its very nature of capitalizing the best aspects of both fixed and sequential procedures. The literature is enriched with various types of partially sequential techniques useable under different types of data set-up. Nonetheless, there is no mention of multivariate data framework in this context, although very common in practice. The present paper aims at developing a class of partially sequential nonparametric test procedures for two-sample multivariate continuou...

On a class of partially sequential two-sample test procedures for multivariate continuous data

Inverse sampling and formal sequential designs may prove useful in reducing the sample size in studies where a small population proportion p is compared with a hypothesized reference proportion p0. These methods are applied to the design of a cytogenetic study about chromosomal abnormalities in men with a daughter affected by Turner's syndrome. First it is shown how the calculated sample size for a classical design depends on the parameterization used. Later this sample size is compared with the required sample size in an inverse sampling design and a triangular sequential design using four different parameterizations (absolute differences, log-odds ratio, angular transform and Sprott's transform). The expected savings in sample size, when the alternative hypothesis is true, are 20% of the fixed sample size for the inverse sampling design and 40% for the triangular sequential design

https://upcommons.upc.edu/bitstream/handle/2099/4168/article.pdf?sequence=4

Víctor Moreno Aguado

Papers

Inverse sampling and triangular sequential designs to compare a small proportion with a reference value