Risk prediction methods

我们继续开发、完善和评估国家癌症研究所乳腺癌风险评估工具（BCRAT）。NCI癌症预防研究员Mateo Banegas博士为拉丁妇女的绝对浸润性乳腺癌风险开发了一种新模型。有兴趣确定是否增加单核苷酸多态性（SNP）的信息可以增加风险模型筛选的判别准确性和有用性。我们证明了通过将SNPs添加到各种更标准的风险因素中来改善乳腺癌的风险分层。 使用来自北方加州（KPNC）的140万名接受HPV检测和巴氏涂片检查的妇女的数据，我们发表了：（1）基于HPV基因分型检测估计绝对风险;（2）计算并比较与新墨西哥州HPV/巴氏涂片登记处的风险;（3）计算巴氏涂片检查结果可疑者的风险。 我们开发并验证了肺癌发病率和死亡风险的模型，并使用它们来预测美国基于风险选择吸烟者进行CT肺癌筛查的影响。 我们开发了适用于筛选检测疾病的左/间隔/右删失数据的绝对风险模型，这些数据发生在电子健康记录数据中，称为“logistic-Weibull”和“logistic-Cox”模型。 我们解释了为什么有时用于评估病例对照数据中绝对风险模型的方法不评估校准，而只是评估相对风险的拟合。

We continued to develop, refine and evaluate the National Cancer Institutes Breast Cancer Risk Assessment Tool (BCRAT). Dr. Mateo Banegas, a NCI Cancer Prevention Fellow, developed a new model for absolute invasive breast cancer risk for Latina women. There is interest in determining whether adding information from single nucleotide polymorphisms (SNPs) can increase the discriminatory accuracy and usefulness for screening of risk models. We demonstrated improved risk stratification of breast cancer by adding SNPs to a variety of more standard risk factors. Using data from 1.4 million women undergoing HPV testing and Pap smears in Kaiser Permanente Northern California (KPNC), we published on: (1) estimating absolute risks based on HPV genotyping tests; (2) calculating and comparing risk versus those in the New Mexico HPV/Pap Registry, and (3) calculating risks among those with equivocal Pap smears. We developed and validated models for risk of lung cancer incidence and death and used them to project the impact of risk-based selection of smokers for CT lung-cancer screening in the US. We developed absolute risk models appropriate under left/interval/right-censoring data occurring for screen-detected disease, which occurs for electronic health record data, called the "logistic-Weibull" and the "logistic-Cox" models. We explained why a method sometimes used to assess absolute risk models in case-control data does not assess calibration, but only fits of relative risk.