 04/2006;

Article: Estimating the induction period of pleural mesothelioma from aggregate data on asbestos consumption.
[Show abstract] [Hide abstract]
ABSTRACT: This study aimed to estimate the induction period from causal action of asbestos exposure to the manifestation of mesothelioma. We included the 9 countries for which we could find published aggregate data on the use of raw asbestos for a relevant time period. We extracted the annual numbers of cases of pleural cancer among men from the World Health Organization mortality database for those years using the International Classification of Diseases, 9th revision, classification. For the Scandinavian countries, we used published national cancer incidence data. In autoregressive Poisson regression modeling, we invoked different time lags of the mean annual use of asbestos to specify which time span produced the best correlation between the 2 time series. The ecologic analysis suggested that the most probable estimate for the mean induction period (use versus morbidity at society level) is approximately 25 years.Journal of Occupational and Environmental Medicine 11/2003; 45(10):110715. · 1.85 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed 'omics' techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves. The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the postprocessing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats. We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated postprocessing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.PLoS ONE 01/2012; 7(4):e34846. · 3.73 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.