5 resultados para Cancer data
em University of Queensland eSpace - Australia
Resumo:
With mixed feature data, problems are induced in modeling the gating network of normalized Gaussian (NG) networks as the assumption of multivariate Gaussian becomes invalid. In this paper, we propose an independence model to handle mixed feature data within the framework of NG networks. The method is illustrated using a real example of breast cancer data.
Resumo:
This paper considers a model-based approach to the clustering of tissue samples of a very large number of genes from microarray experiments. It is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. Frequently in practice, there are also clinical data available on those cases on which the tissue samples have been obtained. Here we investigate how to use the clinical data in conjunction with the microarray gene expression data to cluster the tissue samples. We propose two mixture model-based approaches in which the number of components in the mixture model corresponds to the number of clusters to be imposed on the tissue samples. One approach specifies the components of the mixture model to be the conditional distributions of the microarray data given the clinical data with the mixing proportions also conditioned on the latter data. Another takes the components of the mixture model to represent the joint distributions of the clinical and microarray data. The approaches are demonstrated on some breast cancer data, as studied recently in van't Veer et al. (2002).
Resumo:
Although smoking is widely recognized as a major cause of cancer, there is little information on how it contributes to the global and regional burden of cancers in combination with other risk factors that affect background cancer mortality patterns. We used data from the American Cancer Society's Cancer Prevention Study II (CPS-II) and the WHO and IARC cancer mortality databases to estimate deaths from 8 clusters of site-specific cancers caused by smoking, for 14 epidemiologic subregions of the world, by age and sex. We used lung cancer mortality as an indirect marker for accumulated smoking hazard. CPS-II hazards were adjusted for important covariates. In the year 2000, an estimated 1.42 (95% CI 1.27-1.57) million cancer deaths in the world, 21% of total global cancer deaths, were caused by smoking. Of these, 1.18 million deaths were among men and 0.24 million among women; 625,000 (95% CI 485,000-749,000) smoking-caused cancer deaths occurred in the developing world and 794,000 (95% CI 749,000-840,000) in industrialized regions. Lung cancer accounted for 60% of smoking-attributable cancer mortality, followed by cancers of the upper aerodigestive tract (20%). Based on available data, more than one in every 5 cancer deaths in the world in the year 2000 were caused by smoking, making it possibly the single largest preventable cause of cancer mortality. There was significant variability across regions in the role of smoking as a cause of the different site-specific cancers. This variability illustrates the importance of coupling research and surveillance of smoking with that for other risk factors for more effective cancer prevention. (C) 2005 Wiley-Liss, Inc.
Resumo:
The aim of this study was to apply multifailure survival methods to analyze time to multiple occurrences of basal cell carcinoma (BCC). Data from 4.5 years of follow-up in a randomized controlled trial, the Nambour Skin Cancer Prevention Trial (1992-1996), to evaluate skin cancer prevention were used to assess the influence of sunscreen application on the time to first BCC and the time to subsequent BCCs. Three different approaches of time to ordered multiple events were applied and compared: the Andersen-Gill, Wei-Lin-Weissfeld, and Prentice-Williams-Peterson models. Robust variance estimation approaches were used for all multifailure survival models. Sunscreen treatment was not associated with time to first occurrence of a BCC (hazard ratio = 1.04, 95% confidence interval: 0.79, 1.45). Time to subsequent BCC tumors using the Andersen-Gill model resulted in a lower estimated hazard among the daily sunscreen application group, although statistical significance was not reached (hazard ratio = 0.82, 95% confidence interval: 0.59, 1.15). Similarly, both the Wei-Lin-Weissfeld marginal-hazards and the Prentice-Williams-Peterson gap-time models revealed trends toward a lower risk of subsequent BCC tumors among the sunscreen intervention group. These results demonstrate the importance of conducting multiple-event analysis for recurring events, as risk factors for a single event may differ from those where repeated events are considered.
Resumo:
Objective: To describe the workload profile in a network of Australian skin cancer clinics. Design and setting: Analysis of billing data for the first 6 months of 2005 in a primary-care skin cancer clinic network, consisting of seven clinics and staffed by 20 doctors, located in the Northern Territory, Queensland and New South Wales. Main outcome measures: Consultation to biopsy ratio (CBR); biopsy to treatment ratio (BTR); number of benign naevi excised per melanoma (number needed to treat [NNT]). Results: Of 69780 billed activities, 34 622 (49.6%) were consultations, 19 358 (27.7%) biopsies, 8055 (11.5%) surgical excisions, 2804 (4.0%) additional surgical repairs, 1613 (2.3%) non-surgical treatments of cancers and 3328 (4.8%) treatments of premalignant or non-malignant lesions. A total of 6438 cancers were treated (116 melanomas by excision, 4709 non-melanoma skin cancers [NMSCs] by excision, and 1613 NMSCs non-surgically); 5251 (65.2%) surgical wounds were repaired by direct suture, 2651 (32.9%) by a flap (of which 44.8% were simple flaps), 42 (0.5%) by wedge excision and 111 (1.4%) by grafts. The CBR was 1.79, the BTR was 3.1 and the NNT was 28.6. Conclusions: In this network of Australian skin cancer clinics, one in three biopsies identified a skin cancer (BTR, 3.1), and about 29 benign lesions were excised per melanoma (NNT, 28.6). The estimated NNT was similar to that reported previously in general practice. More data are needed on health outcomes, including effectiveness of treatment and surgical repair.