Risk prediction methods

我们继续发展、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博士开发了拉丁裔女性绝对浸润性乳腺癌风险的新模型。有兴趣确定是否增加信息从单核苷酸多态性（snp）可以增加鉴别准确性和有用性筛选风险模型。我们通过将snp添加到各种更标准的危险因素中，证明了乳腺癌风险分层的改善。使用来自北加州Kaiser Permanente (KPNC) 140万名接受HPV检测和巴氏涂片检查的妇女的数据，我们发表了以下文章：(1)基于HPV基因分型检测估计绝对风险；(2)计算和比较新墨西哥州HPV/Pap登记处的风险，以及(3)计算具有模棱两可的Pap涂片的风险。我们开发并验证了肺癌发病率和死亡风险的模型，并使用它们来预测基于风险选择吸烟者进行CT肺癌筛查的影响。我们开发了绝对风险模型，适用于电子健康记录数据中出现的筛查检测疾病的左/间隔/右审查数据，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.