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Practical problems with clinical guidelines for breast cancer prevention based on remaining lifetime risk.

Quante AS, Whittemore AS, Shriver T, Hopper JL, Strauch K, Terry MB - J. Natl. Cancer Inst. (2015)

Bottom Line: Model sensitivities at thresholds for a 20% false-positive rate were also similar, with 41.8% using IBIS and 38.0% using BOADICEA.RLR-based guidelines for high-risk women are limited by discordance between commonly used risk models.Guidelines based on short-term risks would be more useful, as models are generally developed and validated under a short fixed time horizon (≤10 years).

View Article: PubMed Central - PubMed

Affiliation: : Joseph L. Mailman School of Public Health, Columbia University, New York, NY (ASQ, TS, MBT); Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany (ASQ, KS); Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany (ASQ, KS); Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA (ASW); Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Carlton, Victoria, Australia (JLH); Department of Epidemiology and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, Korea (JLH); Herbert Irving Comprehensive Cancer Center, Columbia Medical Center, New York, NY (MBT).

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Calibration of International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models. The coordinates on the x-axis represent the mean 10-year assigned risks of the IBIS model (left panel) and the BOADICEA model (right panel) within quartiles of assigned risk. The coordinates on the y-axis represent quartile-specific estimates of 10-year breast cancer probabilities based on the women’s observed breast cancer status, and the bars denote 95% confidence intervals for the observed risk. The P value was calculated using a chi-squared goodness-of-fit statistic.
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Figure 2: Calibration of International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models. The coordinates on the x-axis represent the mean 10-year assigned risks of the IBIS model (left panel) and the BOADICEA model (right panel) within quartiles of assigned risk. The coordinates on the y-axis represent quartile-specific estimates of 10-year breast cancer probabilities based on the women’s observed breast cancer status, and the bars denote 95% confidence intervals for the observed risk. The P value was calculated using a chi-squared goodness-of-fit statistic.

Mentions: We cannot address which of the two models gave a better fit (calibration) to the observed breast cancer incidence in the cohort using RLR because it would require observing subjects for their remaining lifetimes. We estimated the 10-year cumulative breast cancer probability to be 5.2% with a 95% confidence interval of 4.2% to 6.4%, compared with the mean of the predicted 10-year assigned risks of 4.9% by IBIS and 3.7% by BOADICEA. When we excluded BRCA1 and BRCA2 carriers (n = 83), we estimated the cumulative breast cancer probability to be 4.8% (95% CI = 3.8% to 6.0%), which exceeds the mean 10-year assigned risk of both models (3.9% for IBIS and 3.0% for BOADICEA (data not shown). We also evaluated model calibration by comparing observed incidence with mean model-assigned risks for subgroups of women defined by quartiles of assigned risk. The four points in each panel of Figure 2 give observed risk (vertical coordinates) and mean assigned risk (horizontal coordinates) for each of the four quartiles of risk determined by IBIS (Figure 2A) and BOADICEA (Figure 2B). The vertical bars represent 95% confidence intervals for the observed risks. Because perfect agreement between observed and assigned risks corresponds to points on the diagonal line, the figure shows better agreement for IBIS than BOADICEA, all of whose assigned risks were lower than those observed. These graphical observations were confirmed by the models’ goodness-of-fit statistics, which are based on the squared vertical distances from the points to the diagonal line (IBIS Χ42= 6.0, P = .20; BOADICEA Χ42= 8.8, P = .07) (Figure 2). When we restricted to BRCA1/2 noncarriers, we found an IBIS Χ42 of 4.2 (P = .37), BOADICEA Χ42of 9.7 (P = .04) (data not shown). We did not observe any differences in calibration by age group (<50 years vs ≥ 50 years) (data not shown).


Practical problems with clinical guidelines for breast cancer prevention based on remaining lifetime risk.

Quante AS, Whittemore AS, Shriver T, Hopper JL, Strauch K, Terry MB - J. Natl. Cancer Inst. (2015)

Calibration of International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models. The coordinates on the x-axis represent the mean 10-year assigned risks of the IBIS model (left panel) and the BOADICEA model (right panel) within quartiles of assigned risk. The coordinates on the y-axis represent quartile-specific estimates of 10-year breast cancer probabilities based on the women’s observed breast cancer status, and the bars denote 95% confidence intervals for the observed risk. The P value was calculated using a chi-squared goodness-of-fit statistic.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4554256&req=5

Figure 2: Calibration of International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models. The coordinates on the x-axis represent the mean 10-year assigned risks of the IBIS model (left panel) and the BOADICEA model (right panel) within quartiles of assigned risk. The coordinates on the y-axis represent quartile-specific estimates of 10-year breast cancer probabilities based on the women’s observed breast cancer status, and the bars denote 95% confidence intervals for the observed risk. The P value was calculated using a chi-squared goodness-of-fit statistic.
Mentions: We cannot address which of the two models gave a better fit (calibration) to the observed breast cancer incidence in the cohort using RLR because it would require observing subjects for their remaining lifetimes. We estimated the 10-year cumulative breast cancer probability to be 5.2% with a 95% confidence interval of 4.2% to 6.4%, compared with the mean of the predicted 10-year assigned risks of 4.9% by IBIS and 3.7% by BOADICEA. When we excluded BRCA1 and BRCA2 carriers (n = 83), we estimated the cumulative breast cancer probability to be 4.8% (95% CI = 3.8% to 6.0%), which exceeds the mean 10-year assigned risk of both models (3.9% for IBIS and 3.0% for BOADICEA (data not shown). We also evaluated model calibration by comparing observed incidence with mean model-assigned risks for subgroups of women defined by quartiles of assigned risk. The four points in each panel of Figure 2 give observed risk (vertical coordinates) and mean assigned risk (horizontal coordinates) for each of the four quartiles of risk determined by IBIS (Figure 2A) and BOADICEA (Figure 2B). The vertical bars represent 95% confidence intervals for the observed risks. Because perfect agreement between observed and assigned risks corresponds to points on the diagonal line, the figure shows better agreement for IBIS than BOADICEA, all of whose assigned risks were lower than those observed. These graphical observations were confirmed by the models’ goodness-of-fit statistics, which are based on the squared vertical distances from the points to the diagonal line (IBIS Χ42= 6.0, P = .20; BOADICEA Χ42= 8.8, P = .07) (Figure 2). When we restricted to BRCA1/2 noncarriers, we found an IBIS Χ42 of 4.2 (P = .37), BOADICEA Χ42of 9.7 (P = .04) (data not shown). We did not observe any differences in calibration by age group (<50 years vs ≥ 50 years) (data not shown).

Bottom Line: Model sensitivities at thresholds for a 20% false-positive rate were also similar, with 41.8% using IBIS and 38.0% using BOADICEA.RLR-based guidelines for high-risk women are limited by discordance between commonly used risk models.Guidelines based on short-term risks would be more useful, as models are generally developed and validated under a short fixed time horizon (≤10 years).

View Article: PubMed Central - PubMed

Affiliation: : Joseph L. Mailman School of Public Health, Columbia University, New York, NY (ASQ, TS, MBT); Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany (ASQ, KS); Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany (ASQ, KS); Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA (ASW); Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, University of Melbourne, Carlton, Victoria, Australia (JLH); Department of Epidemiology and Institute of Health and Environment, School of Public Health, Seoul National University, Seoul, Korea (JLH); Herbert Irving Comprehensive Cancer Center, Columbia Medical Center, New York, NY (MBT).

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Related in: MedlinePlus