Multi-Stage Model Estimates of Lung Cancer Risk from Exposure to Diesel Exhaust, Based on a U.S. Railroad Worker Cohort

A California Environmental Protection Agency (Cal/EPA) report concluded that a reasonable and likely explanation for the increased lung cancer rates in numerous epidemiological studies is a causal association between diesel exhaust exposure and lung cancer. A version of the present analysis, based on a retrospective study of a U.S. railroad worker cohort, provided the Cal/EPA report with some of its estimates of lung cancer risk associated with diesel exhaust. The individual data for that cohort study furnish information on age, employment, and mortality for 56,000 workers over 22 years. Related studies provide information on exposure concentrations. Other analyses of the original cohort data reported finding no relation between measures of diesel exhaust and lung cancer mortality, while a Health Effects Institute report found the data unsuitable for quantitative risk assessment. None of those three works used multistage models, which this article uses in finding a likely quantitative, positive relations between lung cancer and diesel exhaust. A seven-stage model that has the last or next-to-last stage sensitive to diesel exhaust provides best estimates of increase in annual mortality rate due to each unit of concentration, for bracketing assumptions on exposure. Using relative increases of risk and multiplying by the background lung cancer mortality rates for California, the 95% upper confidence limit of the 70-year unit risks for lung cancer is estimated to be in the range 2.1 x 10(-4) (microg/m3)(-1) to 5.5 x 10(-4) (microg/m3)(-1). These risks constitute the low end of those in the Cal/EPA report and are below those reported by previous investigators whose estimates were positive using human data.     

Statistics for discovery

The question is discussed of why investigators in engineering and the physical sciences rarely use statistical methods. It is argued that statistics has in the past been overly influenced by the needs of mathematics rather than those of scientific learning and discovery. Remedies are suggested.     

Nonlinear Autoregression with Positive Innovations

Use of nonlinear models in analyzing time series data is becoming increasingly popular. This paper considers a broad class of nonlinear autoregressive models where the autoregressive part is additive and the terms are nonlinear functions of the past data. Also, the innovation distribution is supported on the non-negative reals and satisfies a tail regularity condition. The linear parameters of the autoregression are estimated using a linear programming recipe which yields much more accurate estimates than traditional methods such as conditional least squares. Limiting distribution of the linear programming estimators is obtained. Simulation studies validate the asymptotic results and reveal excellent small sample properties of the LPE estimator.     

Rethinking Globalization: Glocalization/Grobalization and Something/Nothing

The concept of "grobalization" is proposed to complement the popular idea of "glocalization." In addition, a sociologically relevant concept of "nothing" is defined and juxtaposed with "something." Two continua are created—grobalization‐glocalization and nothing‐something—and their intersection creates four quadrants: the grobalization of nothing, glocalization of nothing, grobalization of something, and glocalization of something. Of greatest importance are the grobalization of nothing and the glocalization of something, as well as the conflict between them. The grobalization of nothing threatens to overwhelm the latter and everything else. Other issues discussed include the loss of something in a world increasingly dominated by nothing, the disappearance of the local, and the relationship of the triumph of nothing to political economy, especially social class. I conclude that no social class is immune to this process and that the poor and lower classess may be "doomed" to something.     

Identifying gaps and blind spots in competitive intelligence

As a result of the intensification and globalization of competition in most fields, the use of competitive intelligence (Cl) in strategic decisions has grown in potential importance. Yet evidence exists that many companies have serious blind spots concerning their competitors' capabilities, intentions and possible reactions to offensive moves. This article introduces a relatively simple but systematic approach to identifying and studying competitive blind spots and facilitating the effective use of Cl in strategic decisions. Applying this approach retrospectively in two companies, and prospectively in a third, we found that if identified serious gaps in Cl, which, in all cases, may have contributed to results shortfalls.     

The recent history and the emerging reality of American sociological theory: A metatheoretical interpretation

This paper has several objectives. First, it demonstrates the utility of employing an array of metatheoretical tools (paradigm, paradigm bridging, micro-macro, integrated paradigm, theoretical syntheses) to analyze the recent history of mainstream American sociological theory. Second, it shows that we have moved through three rather well-defined epochs in sociological theory over the last three decades and are entering a fourth. Third, it demonstrates that the trend in recent years has been away from theoretical separatism and exclusivity, and toward theoretical synthesis and integration. Finally, and relatedly, the integrated sociological paradigm that I sketched a decade ago, or at least something very much like it, it shown to be now much closer to being a reality, perhaps even a defining reality, in sociological theory.