A Bayesian Superpopulation Approach to Inference for Finite Populations Based on Imperfect Diagnostic Outcomes

Geoffrey Jones () and Wesley O. Johnson ()
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Geoffrey Jones: Massey University
Wesley O. Johnson: University of California

Journal of Agricultural, Biological and Environmental Statistics, 2016, vol. 21, issue 2, No 6, 314-327

Abstract: Abstract Common approaches to the analysis of diagnostic outcome data in the absence of a gold standard test employ binomial distributions for the number of infected individuals in each sample, implicitly assuming that the populations are infinite or at least large in relation to the sample size. We present a theoretical framework within which this approach can be justified even for small populations, and describe how this framework can be used to make inferences pertaining to the actual finite populations sampled. The general approach is quite well known in the sample survey literature, but perhaps not outside of it. We show how this approach can be adapted and extended to the modeling of imperfect diagnostic outcomes.

Keywords: Prevalence; Sensitivity; Specificity; Sampling (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s13253-015-0239-9

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