作为乳腺癌风险预测因子的家族聚集测量。

K. Boucher, R. Kerber
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引用次数: 14

摘要

背景:已经提出了几种家族性疾病聚集的测量方法,但其中只有少数被设计用于在个人水平上实施。我们在乳腺癌发病率的背景下评估其中的两个。方法通过连接犹他州人口数据库和犹他州癌症登记处,确定了一个以人群为基础的队列,该队列包括114429名出生于1874年至1931年之间、1965年以后有乳腺癌风险的妇女。采用两种相互竞争的方法来获得家族聚集风险的预测因子:患乳腺癌的一级亲属数量(NIST)和家族标准化发病率(FSIR),后者根据亲属与先证者的亲和程度对亲属的疾病状况进行加权。使用Mantel-Haenszel评估相对风险。泊松回归和样条回归方法。并对年龄相关的风险函数进行了估计。结果与包含91.5%受试者的基线类别相比,使用FSIR标准确定为高风险的0.7%受试者的相对风险约为2.8,而使用NIST标准确定为高风险的受试者的相对风险为2.0。使用任何一种标准,中等风险受试者的相对风险约为1.75。即使对那些没有患病一级亲属的人来说,FSIR也是一个重要的风险预测指标。在老年时,基线风险未见下降。结论sfsir似乎比NIST更能预测乳腺癌风险,特别是对于高危人群。
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Measures of familial aggregation as predictors of breast-cancer risk.
BACKGROUND Several measures of familial disease aggregation have been proposed, but only a few of these are designed to be implemented at the individual level. We evaluate two of them in the context of breast-cancer incidence. METHODS A population-based cohort consisting of 114 429 women born between 1874 and 1931 and at risk for breast cancer after 1965 was identified by linking the Utah Population Data Base and the Utah Cancer Registry. Two competing methods were used to obtain predictors of familial aggregation of risk: the number of first-degree relatives with breast cancer (NIST) and the familial standardised incidence ratio (FSIR), which weights the disease status of relatives based on their degree of relatedness with the proband. Relative risks were estimated using Mantel-Haenszel. Poisson regression and spline regression methods. The age-dependent hazard function was also estimated. RESULTS Compared to a baseline category containing 91.5% of the subjects, the 0.7% of subjects identified as high risk using the FSIR criterion had a relative risk of about 2.8, while those identified as high risk using the NIST criterion had a relative risk of 2.0. Moderate-risk subjects had a relative risk of about 1.75 using either criterion. FSIR was a significant predictor of risk even for those with no affected first-degree relatives. No decline in the baseline risk was observed at advanced ages. CONCLUSIONS FSIR appears to be a better predictor of breast-cancer risk than NIST, particularly for high-risk subjects.
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