BACKGROUND: It is uncertain whether lymphadenectomy (LA) affects overall survival (OS) or disease‐free survival (DFS) rates for patients with stage I nonsmall cell lung cancer (NSCLC), as is the optimal number of lymph nodes that should be recovered. METHODS: There were 24,273 patients with stage I NSCLC diagnosed from 1992 to 2002 who were included in the Surveillance, Epidemiology, and End Results database and who underwent a definitive surgical procedure. Median follow‐up was 35 months. RESULTS: For the entire population, having LA was associated with an increase in the 5‐year OS rate from 41.6% to 58.4% (P<.0001) and in DFS from 58.0% to 73.09%, compared with not having LA. Outcome improved with increasing number of recovered lymph nodes, with a plateau at 11 or more lymph nodes. For patients diagnosed from 1998 to 2002 undergoing only N1 or only N2 dissections, LA was also associated with statistically significant improvements in OS in both groups and a significant difference and trend for improved DFS in the 2 groups, respectively. The maximum differences in both OS and DFS between those with no LA and those with LA occurred when 11 to 16 lymph nodes were removed for the former group or 7 to 10 lymph nodes for the latter group, respectively. CONCLUSIONS: LA was associated with increased rates of OS and DFS, compared with no LA. Our results also suggest the minimum number of recovered lymph nodes needed to see the maximum staging accuracy conferred by LA. Cancer 2009. © 2009 American Cancer Society.

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A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles or particulate matter (PM). Quantifying source-specific risk is a challenge because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model (SEM) typically involves a large number of parameters, for small-sample settings, we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss the implications of our findings with respect to the design of future PM concentrator studies.

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