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博士开发了一种新的模型，用于预测拉美裔女性患浸润性乳腺癌的绝对风险。人们感兴趣的是确定增加单核苷酸多态(SNPs)信息是否可以增加风险模型筛选的判别准确性和有用性。通过将SNPs添加到各种更标准的危险因素中，我们证明了乳腺癌风险分层的改善。利用北加州凯撒永久医院(KPNC)140万名接受HPV检测和巴氏涂片检查的女性的数据，我们发表了以下内容：(1)基于HPV基因分型测试估计绝对风险；(2)计算风险并将其与新墨西哥州HPV/巴氏登记处的风险进行比较；以及(3)计算那些巴氏涂片可疑患者的风险。我们开发并验证了肺癌发病和死亡风险的模型，并使用它们来预测美国CT肺癌筛查中基于风险选择吸烟者的影响。我们建立了适用于筛查疾病发生的左/间隔/右截尾数据下的绝对风险模型，称为Logistic-weible模型和Logistic-Cox模型。我们解释了为什么在病例对照数据中有时用于评估绝对风险模型的方法不评估校正，而只评估相对风险的适合性。

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.