286 research outputs found

    Relative performance of different exposure modeling approaches for sulfur dioxide concentrations in the air in rural western Canada

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>The main objective of this paper is to compare different methods for predicting the levels of SO<sub>2 </sub>air pollution in oil and gas producing area of rural western Canada. Month-long average air quality measurements were collected over a two-year period (2001–2002) at multiple locations, with some side-by-side measurements, and repeated time-series at selected locations.</p> <p>Methods</p> <p>We explored how accurately location-specific mean concentrations of SO<sub>2 </sub>can be predicted for 2002 at 666 locations with multiple measurements. Means of repeated measurements on the 666 locations in 2002 were used as the alloyed gold standard (AGS). First, we considered two approaches: one that uses one measurement from each location of interest; and the other that uses context data on proximity of monitoring sites to putative sources of emission in 2002. Second, we imagined that all of the previous year's (2001's) data were also available to exposure assessors: 9,464 measurements and their context (month, proximity to sources). Exposure prediction approaches we explored with the 2001 data included regression modeling using either mixed or fixed effects models. Third, we used Bayesian methods to combine single measurements from locations in 2002 (not used to calculate AGS) with different <it>priors</it>.</p> <p>Results</p> <p>The regression method that included both fixed and random effects for prediction (Best Linear Unbiased Predictor) had the best agreement with the AGS (Pearson correlation 0.77) and the smallest mean squared error (MSE: 0.03). The second best method in terms of correlation with AGS (0.74) and MSE (0.09) was the Bayesian method that uses normal mixture <it>prior </it>derived from predictions of the 2001 mixed effects applied in the 2002 context.</p> <p>Conclusion</p> <p>It is likely that either collecting some measurements from the desired locations and time periods or predictions of a reasonable empirical mixed effects model perhaps is sufficient in most epidemiological applications. The method to be used in any specific investigation will depend on how much uncertainty can be tolerated in exposure assessment and how closely available data matches circumstances for which estimates/predictions are required.</p

    Improving gene set analysis of microarray data by SAM-GS

    Get PDF
    <p>Abstract</p> <p>Background</p> <p><it>Gene-set </it>analysis evaluates the expression of biological pathways, or <it>a priori </it>defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).</p> <p>Results</p> <p>Using a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with <it>p53 </it>mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of <it>p53</it>. Of the 31 gene sets, 11 actually involve <it>p53 </it>directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of <it>p53 </it>signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with <it>p53</it>.</p> <p>Conclusion</p> <p>We conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use.</p

    Randomized trial of exercise in sedentary middle aged women: effects on quality of life

    Get PDF
    Increasing physical activity is currently considered to be a possible prevention strategy for cancer, obesity, and cardiovascular disease, either alone or in combination with dietary changes. This paper presents results of a randomized trial of moderate-to-vigorous intensity exercise in middle aged, sedentary women; specifically, we report changes in and correlates of quality of life and functional status of this exercise intervention program for both the short (three months) and longer term (12 months). The intervention group showed a significant increase in Mental Health score from baseline to 3 months (p < .01), significantly greater than the change in the control group at 3 months (p < .01). A similar trend among exercisers was observed for the General Health score (p < .01), and this finding was significantly greater than the change in control group at 3 months (p = .01). Change in Social Support – Affection were predictors of the changes in quality of life variables. This study documented improvements in quality of life and general functioning that occurred as a result of participating in an exercise intervention in sedentary middle-aged women