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Epidemiological studies in the information and genomics era: experience of the Clinical Genome of Cancer Project in S?o Paulo, Brazil

机译:信息和基因组学时代的流行病学研究:巴西圣保罗癌症临床基因组计划的经验

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Genomics is expanding the horizons of epidemiology, providing a new dimension for classical epidemiological studies and inspiring the development of large-scale multicenter studies with the statistical power necessary for the assessment of gene-gene and gene-environment interactions in cancer etiology and prognosis. This paper describes the methodology of the Clinical Genome of Cancer Project in S?o Paulo, Brazil (CGCP), which includes patients with nine types of tumors and controls. Three major epidemiological designs were used to reach specific objectives: cross-sectional studies to examine gene expression, case-control studies to evaluate etiological factors, and follow-up studies to analyze genetic profiles in prognosis. The clinical groups included patients' data in the electronic database through the Internet. Two approaches were used for data quality control: continuous data evaluation and data entry consistency. A total of 1749 cases and 1509 controls were entered into the CGCP database from the first trimester of 2002 to the end of 2004. Continuous evaluation showed that, for all tumors taken together, only 0.5% of the general form fields still included potential inconsistencies by the end of 2004. Regarding data entry consistency, the highest percentage of errors (11.8%) was observed for the follow-up form, followed by 6.7% for the clinical form, 4.0% for the general form, and only 1.1% for the pathology form. Good data quality is required for their transformation into useful information for clinical application and for preventive measures. The use of the Internet for communication among researchers and for data entry is perhaps the most innovative feature of the CGCP. The monitoring of patients' data guaranteed their quality.
机译:基因组学正在拓宽流行病学的视野,为经典流行病学研究提供了新的领域,并以评估癌症病因和预后中基因-基因和基因-环境相互作用所需的统计能力,激发了大规模多中心研究的发展。本文介绍了巴西圣保罗的临床癌症基因组计划(CGCP)的方法,该方法包括患有9种类型的肿瘤和对照的患者。三种主要的流行病学设计用于实现特定目标:横断面研究以检查基因表达,病例对照研究以评估病因,以及后续研究以分析预后的遗传特征。临床小组通过互联网将患者的数据包括在电子数据库中。两种方法用于数据质量控制:连续数据评估和数据输入一致性。从2002年头三个月至2004年底,总共CGCP数据库中输入了1749例病例和1509例对照。持续评估表明,对于所有肿瘤,合计,仅0.5%的一般形式字段仍包含潜在的不一致之处。在数据输入一致性方面,随访形式的错误率最高(11.8%),其次是临床形式的6.7%,一般形式的4.0%,仅1.1%。病理形式。为了将它们转化为有用的信息以用于临床应用和预防措施,需要良好的数据质量。利用Internet在研究人员之间进行交流和输入数据可能是CGCP最具创新性的功能。监测患者数据可确保其质量。

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