Simulation study for analysis of binary responses in the presence of extreme case problems.

BACKGROUND: Estimates of variance components for binary responses in presence of extreme case problems tend to be biased due to an under-identified likelihood. The bias persists even when a normal prior is used for the fixed effects. METHODS: A simulation study was carried out to investigate methods for the analysis of binary responses with extreme case problems. A linear mixed model that included a fixed effect and random effects of sire and residual on the liability scale was used to generate binary data. Five simulation scenarios were conducted based on varying percentages of extreme case problems, with true values of heritability equal to 0.07 and 0.17. Five replicates of each dataset were generated and analyzed with a generalized prior (g-prior) of varying weight. RESULTS: Point estimates of sire variance using a normal prior were severely biased when the percentage of extreme case problems was greater than 30%. Depending on the percentage of extreme case problems, the sire variance was overestimated when a normal prior was used by 36 to 102% and 25 to 105% for a heritability of 0.17 and 0.07, respectively. When a g-prior was used, the bias was reduced and even eliminated, depending on the percentage of extreme case problems and the weight assigned to the g-prior. The lowest Pearson correlations between true and estimated fixed effects were obtained when a normal prior was used. When a 15% g-prior was used instead of a normal prior with a heritability equal to 0.17, Pearson correlations between true and fixed effects increased by 11, 20, 23, 27, and 60% for 5, 10, 20, 30 and 75% of extreme case problems, respectively. Conversely, Pearson correlations between true and estimated fixed effects were similar, within datasets of varying percentages of extreme case problems, when a 5, 10, or 15% g-prior was included. Therefore this indicates that a model with a g-prior provides a more adequate estimation of fixed effects. CONCLUSIONS: The results suggest that when analyzing binary data with extreme case problems, bias in the estimation of variance components could be eliminated, or at least significantly reduced by using a g-prior.

Clinical evaluation of highly pathogenic tick-borne flavivirus infection in the mouse model.

The objective of this study was to evaluate the feasibility of using clinical parameters to demonstrate disease progression and differentiate between Omsk hemorrhagic fever virus (OHFV) and Russian spring-summer encephalitis virus (RSSEV) infection in the mouse model. Adult C57BL/6 and balb/c mice were infected with either OHFV or RSSEV by footpad inoculation and their temperature, body weight, clinical signs complete blood count, and blood chemistries were evaluated for up to 15 days post-infection (dpi). Clinical evaluation showed that OHFV infection seriously affects balb/c mice, which had shorter average survival times (ASTs) than other groups. On the contrary, RSSEV infection of C57BL/6 mice was more severe than in balb/c mice. During these studies, the development of fever was not observed and the body weight of OHFV infected balb/c and C57BL/6 mice began to decline sharply starting from day 7 and 8, respectively, which correlated with disease onset. Peak increase of globulin and neutrophils was demonstrated after 9 dpi in OHFV infected mice; however, the lymphocyte number was not affected. Viremia was undetectable in these animals with either virus infection, but virus was found in most organs tested. These results indicate marked differences in the clinical signs, pathology, and immune response of mice infected with either OHFV or RSSEV and further validate the use of this mouse model system to evaluate human disease.