VALIDATION OF THE GAIL ET-AL MODEL FOR PREDICTING INDIVIDUAL BREAST-CANCER RISK
VALIDATION OF THE GAIL ET-AL MODEL FOR PREDICTING INDIVIDUAL BREAST-CANCER RISK
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DOI：
10.1093/jnci/86.8.600
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发表时间：
1994-04-20
期刊：
影响因子：
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通讯作者：
HERTZMARK, E
中科院分区：
文献类型：
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作者：
SPIEGELMAN, D;COLDITZ, GA;HERTZMARK, E
背景：Gall等。模型被认为是估计女性患乳腺癌风险的最佳方法。此类估计对于妇女，设计预防试验以及针对筛查和预防工作的决策很有用。目的：我们的目的是使用与模型得出的大量人群相关的大量人群来评估模型准确预测乳腺癌风险的能力。方法：我们将模型预测的癌症病例数与护士健康研究中观察到的实际病例数进行了比较。研究人群为115 172名在研究开始时没有乳腺癌的女性。问卷每两年发送一次参与者，以寻求有关危险因素和诊断乳腺癌的数据。在12年的研究期间，随访的依从性为95％。结果：模型过度预测的绝对乳腺癌风险高33％（95％置信区间[CI] = 28％-39％），在绝经前妇女中预测过度预测超过双重（95％CI = 1.9-2.2），患有女性乳腺癌的广泛家族病史（95％CI = 1.1-3.9），在20岁以下的妇女中，有年龄（95％CI =） 1.3-4.7）。观察到的风险和预测风险之间的相关系数为0.67，表明该模型对单个乳腺癌风险的排名不足。预测过度发生在所有预测风险的十分位置。结论：该模型的表现对于不参加年度筛查的25-61岁的个体女性估计乳腺癌的风险并不令人满意。护士健康研究中乳房X线摄影筛查率较低可能是观察到的病例和预测病例之间的差异，但不是全部。含义：他莫昔芬试验研究者对模型的最新修改很可能提供了准确的功率计算。该模型的这种修改形式对于计划其他大型，基于人群的研究应该很有用。
Background: The Gall et al. model is considered the best available means for estimating an individual woman's risk of developing breast cancer. Such estimates are useful in decision making on the part of women, in designing prevention trials, and in targeting screening and prevention efforts. Purpose: Our purpose was to evaluate the ability of the model to accurately predict individual breast cancer risk, using a large population independent of the one from which the model was derived. Methods: We compared the number of cancer cases predicted by the model to the actual number of cases observed in the Nurses' Health Study. The study population was 115 172 women who did not have breast cancer at the beginning of the study. Questionnaires were sent to participants every 2 years, seeking data on risk factors and diagnoses of breast cancer. Follow-up compliance was 95% over the 12-year study period. Results: The model overpredicted absolute breast cancer risk by 33% (95% confidence interval [CI] = 28%-39%), with the overprediction more than twofold among premenopausal women (95% CI = 1.9-2.2), among women with extensive family history of breast cancer (95% CI = 1.1-3.9), and among women with age at first birth younger than 20 years (95% CI = 1.3-4.7). The correlation coefficient between observed and predicted risk was 0.67, indicating that the model is less than satisfactory for ranking individual levels of breast cancer risk. Overprediction occurred at all deciles of predicted risk. Conclusions: The model's performance is unsatisfactory for estimating breast cancer risk for individual women aged 25-61 years who do not participate in annual screening. Lower mammography screening rates in the Nurses' Health Study may account for some, but not all, of the discrepancy between observed and predicted cases. Implications: A recent modification of the model by the tamoxifen trial investigators is likely to have provided accurate power calculations. This modified form of the model should be useful for planning other large, population-based studies.