This paper presents several models for investigating whether the HLA allogenotypes DR1/Br, DR3 and DR10 are genetic markers for a predisposition of experiencing unexplained recurrent fetal losses. A total of 199 women from 113 families answered questionnaires concerning their pregnancies and 145 of these women were HLA typed. The analysis of the data is complicated as dependencies between pregnancy outcomes are expected.

In this study, we explore the relationship between democracy and international trade and demonstrate that the link is weak. We base our analysis on a general equilibrium model of international trade as well as on corrected, symmetric measures of bilateral trade flows. Further, we also incorporate spatial dependence among the observations. We argue that previous studies which show that democratic countries are more likely to trade with each other rely on a misspecification of the trade model. Once this misspecification is corrected, democracy is no longer a significant direct influence on bilateral trade flows.

Abstract Methodological aspects of meta-analytic practice, heterogeneity, publication bias, metaregression and effect metric, were investigated in 14 meta-analyses reflecting major therapeutic concern in Critical Care practice. Compared with the standard Q test, the exact Zelen test was more sensitive in identifying heterogeneity. Assessment of heterogeneity impact by the I 2 statistic was consistent with inferences afforded by both the Q and Zelen test. Publication bias was subject to test and metric determination: funnel plots exhibited variable asymmetry across studies and between metrics; the regression asymmetry test appeared more sensitive than the rank correlation test; the “trim and fill ” method was the most sensitive, but suggested, on the basis of quantification of the effects of potentially missing studies, that meta-analyses may be resistant to such missingness. Metaregression of treatment effect against control risk using Bayesian hierarchical regression in all metrics (log odds ratio, log risk ratio and RD) suggested that naïve linear regression approaches over-diagnosed significant relationships and exhibited regression dilution. Heterogeneity, publication bias and risk related treatment effects all demonstrate estimator and metric dependence; the RD metric would appear the most capricious in this regard.

The data in Figure 1 were obtained by asking 51 subjects ranging in age from 8 to 73 years to track, using jaw movements alone, a dot moving sinusoidally in one dimension at.6 Hz on a video monitor. Their jaw movements were captured electronically by means of strain guages and translated into the motion of a cursor which was to track (follow) the moving dot. Fidelity of tracking was measured in several ways, including TTD, the RMS difference between target and tracker shown in Figure 1. Low values of TTD indicate that the subject has high control of the speech articulator ((lips, jaw, or voice). The investigators believed ability to control speech articulators to be unimodal in age, reaching a broad optimum in early to middle adulthood. Hence, they wished to fit unimodal regressions to such data. The purpose was to establish agenorms against which to compare the performance of neurologically compromised individuals.

BACKGROUND: One problem of interpreting population-based biomonitoring data is the reconstruction of corresponding external exposure in cases where no such data are available. OBJECTIVES: We demonstrate the use of a computational framework that integrates physiologically based pharmacokinetic (PBPK) modeling, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of environmental chloroform source concentrations consistent with human biomonitoring data. The biomonitoring data consist of chloroform blood concentrations measured as part of the Third National Health and Nutrition Examination Survey (NHANES III), and for which no corresponding exposure data were collected. METHODS: We used a combined PBPK and shower exposure model to consider several routes and sources of exposure: ingestion of tap water, inhalation of ambient household air, and inhalation and dermal absorption while showering. We determined posterior distributions for chloroform concentration in tap water and ambient household air using U.S. Environmental Protection Agency Total Exposure Assessment Methodology (TEAM) data as prior distributions for the Bayesian analysis. RESULTS: Posterior distributions for exposure indicate that 95 % of the population represented by the NHANES III data had likely chloroform exposures ≤ 67 µg/L in tap water and ≤ 0.02 µg/L in ambient household air. CONCLUSIONS: Our results demonstrate the application of computer simulation to aid in the interpretation of human biomonitoring data in the context of the exposure–health evaluation–risk assessment continuum. These results should be considered as a demonstration of the method and can be improved with the addition of more detailed data.