IA04:简化Rosner-Colditz乳腺癌发病率模型在加州教师研究中的验证

B. Rosner
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Correlated AUC methods (Rosner and Glynn, 2009) were used to compare AUC9s of competing models. Calibration was assessed by using relative risks from the RC and Gail models and absolute incidence rates from SEER. Results: Variables considered in the RC models were age; age at menopause (by type of menopause), menopausal status, age at 1 st birth age at menarche, nulliparity, birth index, benign breast disease, duration of HRT use among current users by type of HRT, weight at age 18, change in weight from age 18 to baseline, separately by menopausal status and HRT use, height, alcohol consumption and family Hx of breast cancer. Age-adjusted AUC estimates in the NHS population were: (14-year risk model, RC model: 0.606 ± 0.005, Gail model: 0.563 ± 0.005, p diff diff Age-adjusted AUC estimates in the validation (CTS) population were: (14-year risk model, RC model: 0.580 ± 0.007, Gail model: 0.549 ± 0.007, p diff diff =0.025). Calibration of the 14-year risk model indicated an estimated E/O ratio of 1.10 (95% CI = 1.05, 1.15) for RC; 1.08 (95% CI = 1.05-1.13) for Gail. Calibration of the 4-year risk model indicated an estimated E/O ratio of 1.16 (95% CI = 1.07-1.26) for RC; 1.15 (95% CI = 1.07-1.25) for Gail. Calibration results were similar using Poisson regression. Conclusion: The simplified RC model based on baseline risk factors is practical to use in a clinical setting and has a significantly higher AUC than the Gail model when validated in an external sample. AUC is better for short-term (4-year) vs. long-term risk prediction. Calibration is slightly off using both models and indicates that expected risks are slightly higher than observed risks for both short-term and long-term models. Citation Format: Bernard A. Rosner. Validation of a simplified Rosner-Colditz breast cancer incidence model in the California Teachers9 Study. [abstract]. 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Correlated AUC methods (Rosner and Glynn, 2009) were used to compare AUC9s of competing models. Calibration was assessed by using relative risks from the RC and Gail models and absolute incidence rates from SEER. Results: Variables considered in the RC models were age; age at menopause (by type of menopause), menopausal status, age at 1 st birth age at menarche, nulliparity, birth index, benign breast disease, duration of HRT use among current users by type of HRT, weight at age 18, change in weight from age 18 to baseline, separately by menopausal status and HRT use, height, alcohol consumption and family Hx of breast cancer. Age-adjusted AUC estimates in the NHS population were: (14-year risk model, RC model: 0.606 ± 0.005, Gail model: 0.563 ± 0.005, p diff diff Age-adjusted AUC estimates in the validation (CTS) population were: (14-year risk model, RC model: 0.580 ± 0.007, Gail model: 0.549 ± 0.007, p diff diff =0.025). Calibration of the 14-year risk model indicated an estimated E/O ratio of 1.10 (95% CI = 1.05, 1.15) for RC; 1.08 (95% CI = 1.05-1.13) for Gail. Calibration of the 4-year risk model indicated an estimated E/O ratio of 1.16 (95% CI = 1.07-1.26) for RC; 1.15 (95% CI = 1.07-1.25) for Gail. Calibration results were similar using Poisson regression. Conclusion: The simplified RC model based on baseline risk factors is practical to use in a clinical setting and has a significantly higher AUC than the Gail model when validated in an external sample. AUC is better for short-term (4-year) vs. long-term risk prediction. Calibration is slightly off using both models and indicates that expected risks are slightly higher than observed risks for both short-term and long-term models. Citation Format: Bernard A. Rosner. Validation of a simplified Rosner-Colditz breast cancer incidence model in the California Teachers9 Study. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. 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引用次数: 0

摘要

目的:在独立数据集中验证使用基线危险因素的简化乳腺癌发病率模型方法:我们将研究人群限制在基线时可比较的年龄范围(47-79岁)(Nurses9健康研究(NHS), 1994, n=64,627;我们使用基线风险因素拟合简化的Rosner-Colditz (RC)对数发病率模型,并基于NHS数据估计14年风险模型(1994-2008年,3597例)和4年风险模型(1994-1998年,1616例)。将14年和4年风险模型与Gail模型在同一时期的CTS人群中进行比较(14年模型,1995-2009年,1786例;4年风险模型,1995-1999,543例)。我们使用基于AUC的判别方法和基于泊松回归的校准方法来评估性能。使用相关AUC方法(Rosner and Glynn, 2009)比较竞争模型的AUC。通过使用RC和Gail模型的相对风险以及SEER的绝对发病率来评估校准。结果:RC模型中考虑的变量有年龄;绝经年龄(按绝经类型)、绝经状态、初潮时的第1个出生年龄、无产、出生指数、良性乳腺疾病、当前HRT使用者按HRT类型使用HRT的持续时间、18岁时的体重、从18岁到基线的体重变化,分别按绝经状态和HRT使用、身高、饮酒量和乳腺癌的家族Hx。NHS人群的年龄调整AUC估计值为:(14年风险模型,RC模型:0.606±0.005,Gail模型:0.563±0.005,p diff diff =0.025)验证(CTS)人群的年龄调整AUC估计值为:(14年风险模型,RC模型:0.580±0.007,Gail模型:0.549±0.007,p diff diff =0.025)。校正14年风险模型表明,RC的估计E/O比为1.10 (95% CI = 1.05, 1.15);Gail为1.08 (95% CI = 1.05-1.13)。校正4年风险模型表明,RC的估计E/O比率为1.16 (95% CI = 1.07-1.26);Gail为1.15 (95% CI = 1.07-1.25)。用泊松回归校正结果相似。结论:基于基线危险因素的简化RC模型在临床环境中是实用的,并且在外部样本中验证时,其AUC明显高于Gail模型。AUC对短期(4年)风险预测优于长期风险预测。两种模型的校准都略有偏差,表明短期和长期模型的预期风险略高于观察到的风险。引用格式:Bernard A. Rosner。简化Rosner-Colditz乳腺癌发病率模型在加州教师研究中的验证[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr IA04。
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Abstract IA04: Validation of a simplified Rosner-Colditz breast cancer incidence model in the California Teachers' Study
Purpose: To validate a simplified breast cancer incidence model using baseline risk factors in an independent dataset Methods: We restricted the study population to comparable age ranges at baseline (age 47-79) (Nurses9 Health Study (NHS), 1994, n=64,627; California Teachers9 Study (CTS), 1995, n=31,386) We fit simplified Rosner-Colditz (RC) log incidence models using baseline risk factors and estimated both a 14-year risk model (1994-2008, 3597 cases) and a 4-year risk model (1994-1998, 1616 cases) based on NHS data. Both the 14-year and 4-year risk models were compared with the Gail model over the same time periods in the CTS population (14-year model, 1995-2009, 1786 cases; 4-year risk model, 1995-1999, 543 cases). We assessed performance using measures of discrimination based on AUC and calibration based on Poisson regression. Correlated AUC methods (Rosner and Glynn, 2009) were used to compare AUC9s of competing models. Calibration was assessed by using relative risks from the RC and Gail models and absolute incidence rates from SEER. Results: Variables considered in the RC models were age; age at menopause (by type of menopause), menopausal status, age at 1 st birth age at menarche, nulliparity, birth index, benign breast disease, duration of HRT use among current users by type of HRT, weight at age 18, change in weight from age 18 to baseline, separately by menopausal status and HRT use, height, alcohol consumption and family Hx of breast cancer. Age-adjusted AUC estimates in the NHS population were: (14-year risk model, RC model: 0.606 ± 0.005, Gail model: 0.563 ± 0.005, p diff diff Age-adjusted AUC estimates in the validation (CTS) population were: (14-year risk model, RC model: 0.580 ± 0.007, Gail model: 0.549 ± 0.007, p diff diff =0.025). Calibration of the 14-year risk model indicated an estimated E/O ratio of 1.10 (95% CI = 1.05, 1.15) for RC; 1.08 (95% CI = 1.05-1.13) for Gail. Calibration of the 4-year risk model indicated an estimated E/O ratio of 1.16 (95% CI = 1.07-1.26) for RC; 1.15 (95% CI = 1.07-1.25) for Gail. Calibration results were similar using Poisson regression. Conclusion: The simplified RC model based on baseline risk factors is practical to use in a clinical setting and has a significantly higher AUC than the Gail model when validated in an external sample. AUC is better for short-term (4-year) vs. long-term risk prediction. Calibration is slightly off using both models and indicates that expected risks are slightly higher than observed risks for both short-term and long-term models. Citation Format: Bernard A. Rosner. Validation of a simplified Rosner-Colditz breast cancer incidence model in the California Teachers9 Study. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA04.
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