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Objective: To examine the association between occupational exposure to silica and lung cancer from a systematic review (and meta-analysis) of the epidemiologic literature, with special reference to the methodological quality of observational studies., Methods: We searched Medline, Toxline, BIOSIS, and Embase (1966-December 2007) for original articles published in any language. Observational studies (cohort and case-control studies) were selected if they reported the result of dose-response analyses relating lung cancer to occupational exposure to silica after appropriate adjustment for smoking., Results: Ten studies (4 cohort studies and 6 case-control studies) met the inclusion criteria of the meta-analysis, nine of which contributing to the main analysis (dose-response analysis, no lag time). We found increasing risk of lung cancer with increasing cumulative exposure to silica, with heterogeneity across studies however. Posthoc analyses identified a set of seven more homogeneous studies. Their meta-analysis resulted in a dose-response curve that was not different from that obtained in the main analysis., Conclusion: Silica is a lung carcinogen. This increased risk is particularly apparent when the cumulative exposure to silica is well beyond that resulting from exposure to the recommended limit concentration for a prolonged period of time.

In many follow-up studies, patients are subject to concurrent events. In this article, we consider semicompeting risks data as defined by Fine, Jiang, and Chappell (2001, Biometrika 88, 907-919) where one event is censored by the other but not vice versa. The proposed model involves marginal survival functions for the two events and a parametric family of copulas for their dependency. This article suggests a general method for estimating the dependence parameter when the dependency is modeled with an Archimedean copula. It uses the copula-graphic estimator of Zheng and Klein (1995, Biometrika 82, 127-138) for estimating the survival function of the nonterminal event, subject to dependent censoring. Asymptotic properties of these estimators are derived. Simulations show that the new methods work well with finite samples. The copula-graphic estimator is shown to be more accurate than the estimator proposed by Fine et al. (2001); its performances are similar to those of the self-consistent estimator of Jiang, Fine, Kosorok, and Chappell (2005, Scandinavian Journal of Statistics 33, 1-20). The analysis of a data set, emphasizing the estimation of characteristics of the observable region, is presented as an illustration.