An Exposure‐Response Threshold for Lung Diseases and Lung Cancer Caused by Crystalline Silica

Whether crystalline silica (CS) exposure increases risk of lung cancer in humans without silicosis, and, if so, whether the exposure‐response relation has a threshold, have been much debated. Epidemiological evidence is ambiguous and conflicting. Experimental data show that high levels of CS cause lung cancer in rats, although not in other species, including mice, guinea pigs, or hamsters; but the relevance of such animal data to humans has been uncertain. This article applies recent insights into the toxicology of lung diseases caused by poorly soluble particles (PSPs), and by CS in particular, to model the exposure‐response relation between CS and risk of lung pathologies such as chronic inflammation, silicosis, fibrosis, and lung cancer. An inflammatory mode of action is described, having substantial empirical support, in which exposure increases alveolar macrophages and neutrophils in the alveolar epithelium, leading to increased reactive oxygen species (ROS) and nitrogen species (RNS), pro‐inflammatory mediators such as TNF‐alpha, and eventual damage to lung tissue and epithelial hyperplasia, resulting in fibrosis and increased lung cancer risk among silicotics. This mode of action involves several positive feedback loops. Exposures that increase the gain factors around such loops can create a disease state with elevated levels of ROS, TNF‐alpha, TGF‐beta, alveolar macrophages, and neutrophils. This mechanism implies a “tipping point” threshold for the exposure‐response relation. Applying this new model to epidemiological data, we conclude that current permissible exposure levels, on the order of 0.1 mg/m³, are probably below the threshold for triggering lung diseases in humans.     

Confidence Intervals for a Binomial Parameter after Observing No Successes

The easily computed, one-sided confidence interval for the binomial parameter provides the basis for an interesting classroom example of scientific thinking and its relationship to confidence intervals. The upper limit can be represented as the sample proportion from a number of “successes” in a future experiment of the same sample size. The upper limit reported by most people corresponds closely to that producing a 95 percent classical confidence interval and has a Bayesian interpretation.     

Accommodating stochastic departures from percentile invariance in causal models

Summary.  Consider a clinical trial in which participants are randomized to a single-dose treatment or a placebo control and assume that the adherence level is accurately recorded. If the treatment is effective, then good adherers in the treatment group should do better than poor ad- herers because they received more drug; the treatment group data follow a dose–response curve. But, good adherers to the placebo often do better than poor adherers, so the observed adherence–response in the treatment group cannot be completely attributed to the treatment. Efron and Feldman proposed an adjustment to the observed adherence–response in the treatment group by using the adherence–response in the control group. It relies on a percentile invariance assumption under which each participant's adherence percentile within their assigned treatment group does not depend on the assigned group (active drug or placebo). The Efron and Feldman approach is valid under percentile invariance, but not necessarily under departures from it. We propose an analysis based on a generalization of percentile invariance that allows adherence percentiles to be stochastically permuted across treatment groups, using a broad class of stochastic permutation models. We show that approximate maximum likelihood estimates of the underlying dose–response curve perform well when the stochastic permutation process is correctly specified and are quite robust to model misspecification.     

Why a time effect often has a limited impact on capture‐recapture estimates in closed populations

The author is concerned with log‐linear estimators of the size N of a population in a capture‐recapture experiment featuring heterogeneity in the individual capture probabilities and a time effect. He also considers models where the first capture influences the probability of subsequent captures. He derives several results from a new inequality associated with a dispersive ordering for discrete random variables. He shows that in a log‐linear model with inter‐individual heterogeneity, the estimator N is an increasing function of the heterogeneity parameter. He also shows that the inclusion of a time effect in the capture probabilities decreases N in models without heterogeneity. He further argues that a model featuring heterogeneity can accommodate a time effect through a small change in the heterogeneity parameter. He demonstrates these results using an inequality for the estimators of the heterogeneity parameters and illustrates them in a Monte Carlo experiment     

The conical structure of adjustive behavior

Wellbeing and coping have usually been treated as separate topics in previous research. The present study proposes a general framework for adjustive behavior which incorporates both previous topics as special cases. This framework shows definitional similarities and differences among wellbeing, coping and further special cases. Empirical data are presented which show a correspondence between the facets of the definitional system and the matrix of intercorrelations of the variables defined thereby. The correspondence is that of a conex, that is, the facets correspond to a conical coordinate system for the SSA space that reproduces the correlation matrix.The data were gathered as part of the Continuing Survey conducted jointly by the Israel Institute of Applied Social Research and the Hebrew University's Communications Institute. The population sampled is the adult urban and rural Jewish population of Israel, over 20 years of age in mid-December 1977. Interviews were conducted in the home through closed questionnaires, 27 of the items being of adjustive behavior.