Estimating Animal Abundance in Ground Beef Batches Assayed with Molecular Markers

Estimating animal abundance in industrial scale batches of ground meat is important for mapping meat products through the manufacturing process and for effectively tracing the finished product during a food safety recall. The processing of ground beef involves a potentially large number of animals from diverse sources in a single product batch, which produces a high heterogeneity in capture probability. In order to estimate animal abundance through DNA profiling of ground beef constituents, two parameter-based statistical models were developed for incidence data. Simulations were applied to evaluate the maximum likelihood estimate (MLE) of a joint likelihood function from multiple surveys, showing superiority in the presence of high capture heterogeneity with small sample sizes, or comparable estimation in the presence of low capture heterogeneity with a large sample size when compared to other existing models. Our model employs the full information on the pattern of the capture-recapture frequencies from multiple samples. We applied the proposed models to estimate animal abundance in six manufacturing beef batches, genotyped using 30 single nucleotide polymorphism (SNP) markers, from a large scale beef grinding facility. Results show that between 411∼1367 animals were present in six manufacturing beef batches. These estimates are informative as a reference for improving recall processes and tracing finished meat products back to source

Estimation of population size when capture probability depends on individual state

We develop a multi-state model to estimate the size of a closed population from capture–recapture studies. We consider the case where capture–recapture data are not of a simple binary form, but where the state of an individual is also recorded upon every capture as a discrete variable. The proposed multi-state model can be regarded as a generalisation of the commonly applied set of closed population models to a multi-state form. The model allows for heterogeneity within the capture probabilities associated with each state while also permitting individuals to move between the different discrete states. A closed-form expression for the likelihood is presented in terms of a set of sufficient statistics. The link between existing models for capture heterogeneity is established, and simulation is used to show that the estimate of population size can be biased when movement between states is not accounted for. The proposed unconditional approach is also compared to a conditional approach to assess estimation bias. The model derived in this paper is motivated by a real ecological data set on great crested newts, Triturus cristatus. Supplementary materials accompanying this paper appear online

Inferred referendum: a rule for committee decisions

A new method of social choice is presented. The result of the method coincides with that of majority voting when it does not produce an intransitivity among the alternatives under consideration. When majority voting would produce an intransitivity, the method orders the alternatives in the same way as the transitive constituency would whom the committee members are most likely to represent. Analysis of the application of the method to three alternatives shows that the resulting order depends only on the committee members' votes between pairs of alternatives; the resulting order is less sensitive to irrelevant alternatives than the orders provided by other schemes; when majority voting provides an intransitivity, the hypothesis that, in fact, the committee's constituency is as assumed is almost as likely as the hypothesis that it precisely mirrors the committee

An iterative estimating procedure for probit-type nonresponse models in surveys with call backs

Conditioned likelihood, iterative estimation, missing data, non ignorability, non response, probit models, 62A10, 62E20, 62F12, 62F30,

Comparison of dose-finding designs for narrow-therapeutic-index drugs: Concentration-controlled vs. dose-controlled trials

This study compared the performances of randomized dose-controlled trials (DCTs) with those of concentration-controlled trials (CCTs) in dose finding for drugs with narrow therapeutic indexes. A simulation-based study was performed for a hypothetical immunosuppressant agent with two clinical end points. Different scenarios were simulated and analyzed, and three designs were compared: one DCT and two CCTs (a target-equivalent CCT and a variability-equivalent CCT). The DCT was consistently superior to the CCTs in the following aspects: (i) precision and bias reduction in parameter estimates, (ii) precision and bias reduction in the estimate of optimal exposure, (iii) bias reduction in prediction of the estimated therapeutic benefit at estimated optimal exposure, and (iv) bias reduction in prediction of the estimated benefit of therapeutic drug monitoring as compared with fixed dosing. DCT designs are more informative when describing the exposure-response relationship for drugs with narrow therapeutic indexes and provide a better basis for decision making with regard to dosing strategy

Integrated methodology for multiple systems estimation and record linkage using a missing data formulation

Capture–recapture, Heterogeneity, Data fusion, EM algorithm, Fellegi–Sunter linkage, Missing data,