Mendelian risk prediction models calculate the probability of a proband being a mutation carrier based on family history and known mutation prevalence and penetrance. Family history in this setting, is self-reported and is often reported with error. Various studies in the literature have evaluated misreporting of family history. Using a validation data set which includes both error-prone self-reported family history and error-free validated family history, we propose a method to adjust for misreporting of family history. We estimate the measurement error process in a validation data set (from University of California at Irvine (UCI)) using nonparametric smoothed Kaplan-Meier estimators, and use Monte Carlo integration to implement the adjustment. In this paper, we extend BRCAPRO, a Mendelian risk prediction model for breast and ovarian cancers, to adjust for misreporting in family history. We apply the extended model to data from the Cancer Genetics Network (CGN).
Propensity score methods are widely used to analyze observational studies in which patient characteristics might not be balanced by treatment group. These methods assume that exposure, or treatment assignment, is error-free, but in reality these variables can be subject to measurement error. This arises in the context of comparative effectiveness research, using observational administrative claims data in which accurate procedural codes are not always available. When using propensity score based methods, this error affects both the exposure variable directly, as well as the propensity score. We propose a two step maximum likelihood approach using validation data to adjust for the measurement error. First, we use a likelihood approach to estimate an adjusted propensity score. Using the adjusted propensity score, we then use a likelihood approach on the outcome model to adjust for measurement error in the exposure variable directly. In addition, we show the bias introduced when using error-prone treatment in the inverse probability weighting (IPW) estimator and propose an approach to eliminate this bias. Simulations show our proposed approaches reduce the bias and mean squared error (MSE) of the treatment effect estimator compared to using the error-prone treatment assignment.
Measurement error in time to event data used as a predictor will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event predictor often measured with error, used in Mendelian risk prediction models. Using a validation data set, we propose a method to adjust for this type of measurement error. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian risk prediction models and multivariate survival prediction models, as well as illustrate our method using a data application for Mendelian risk prediction models. Results from simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our adjusted method mitigates the effects of measurement error mainly by improving calibration and by improving total accuracy. In some scenarios discrimination is also improved.
Numerous models have been developed to quantify the combined effect of various risk factors to predict either risk of developing breast cancer, risk of carrying a high-risk germline genetic mutation, specifically in the BRCA1 and BRCA2 genes, or the risk of both. These breast cancer risk models can be separated into those that utilize mainly hormonal and environmental factors and those that focus more on hereditary risk. Given the wide range of models from which to choose, understanding what each model predicts, the populations for which each is best suited to provide risk estimations, the current validation and comparative studies that have been performed for each model, and how to apply them practically is important for clinicians and researchers seeking to utilize risk models in their practice. This review provides a comprehensive guide for those seeking to understand and apply breast cancer risk models by summarizing the majority of existing breast cancer risk prediction models including the risk factors they incorporate, the basic methodology in their development, the information each provides, their strengths and limitations, relevant validation studies, and how to access each for clinical or investigative purposes.
The integrated discrimination improvement (IDI) is commonly used to compare two risk prediction models; it summarizes the extent a new model increases risk in events and decreases risk in non-events. The IDI averages risks across events and non-events and is therefore susceptible to Simpson's paradox. In some settings, adding a predictive covariate to a well calibrated model results in an overall negative (positive) IDI. However, if stratified by that same covariate, the strata-specific IDIs are positive (negative). Meanwhile, the calibration (observed to expected ratio and Hosmer–Lemeshow Goodness of Fit Test), area under the receiver operating characteristic curve, and Brier score improve overall and by stratum. We ran extensive simulations to investigate the impact of an imbalanced covariate upon metrics (IDI, area under the receiver operating characteristic curve, Brier score, and R2), provide an analytic explanation for the paradox in the IDI, and use an investigative metric, a Weighted IDI, to better understand the paradox. In simulations, all instances of the paradox occurred under stratum-specific mis-calibration, yet there were mis-calibrated settings in which the paradox did not occur. The paradox is illustrated on Cancer Genomics Network data by calculating predictions based on two versions of BRCAPRO, a Mendelian risk prediction model for breast and ovarian cancer. In both simulations and the Cancer Genomics Network data, overall model calibration did not guarantee stratum-level calibration. We conclude that the IDI should only assess model performance among a clinically relevant subset when stratum-level calibration is strictly met and recommend calculating additional metrics to confirm the direction and conclusions of the IDI.
Background: The benefit of adjuvant chemotherapy in postmenopausal patients with estrogen receptor (ER)-positive lymph node-negative breast cancer is being reassessed.
Patients and methods: After stratification by ER status, 1669 postmenopausal patients with operable lymph node-negative breast cancer were randomly assigned to three 28-day courses of ‘classical’ CMF (cyclophosphamide, methotrexate, 5-fluorouracil) chemotherapy followed by tamoxifen for 57 months (CMF→tamoxifen) or to tamoxifen alone for 5 years.
Results: ERs were positive in 81% of tumors. At a median follow-up of 13.1 years, patients with ER-positive breast cancers did not benefit from CMF [13-year disease-free survival (DFS) 64% CMF→tamoxifen, 66% tamoxifen; P = 0.99], whereas CMF substantially improved the prognosis of patients with ER-negative breast cancer (13-year DFS 73% versus 57%, P = 0.001). Similarly, breast cancer-free interval (BCFI) was identical in the ER-positive cohort but significantly improved by chemotherapy in the ER-negative cohort (13-year BCFI 80% versus 63%, P = 0.001). CMF had no influence on second nonbreast malignancies or deaths from other causes.
Conclusion: CMF is not beneficial in postmenopausal patients with node-negative ER-positive breast cancer but is highly effective within the ER-negative cohort. In the future, other markers of chemotherapy response may define a subset of patients with ER-positive tumors who may benefit from adjuvant chemotherapy.
Keywords: adjuvant chemotherapy, breast cancer, estrogen receptor, postmenopause, tamoxifen
Background: The International Breast Cancer Study Group Trial VIII compared long-term efficacy of endocrine therapy (goserelin), chemotherapy [cyclophosphamide, methotrexate and fluorouracil (CMF)], and chemoendocrine therapy (CMF followed by goserelin) for pre/perimenopausal women with lymph-node-negative breast cancer.
Patients and methods: From 1990 to 1999, 1063 patients were randomized to receive (i) goserelin for 24 months (n = 346), (ii) six courses of ‘classical’ CMF (cyclophosphamide, methotrexate, 5-fluorouracil) chemotherapy (n = 360), or (iii) six courses of CMF plus 18 months goserelin (CMF→ goserelin; n = 357). Tumors were classified as estrogen receptor (ER) negative (19%), ER positive (80%), or ER unknown (1%); 19% of patients were younger than 40. Median follow-up was 12.1 years.
Results: For the ER-positive cohort, sequential therapy provided a statistically significant benefit in disease-free survival (DFS) (12-year DFS = 77%) compared with CMF alone (69%) and goserelin alone (68%) (P = 0.04 for each comparison), due largely to the effect in younger patients. Patients with ER-negative tumors whose treatment included CMF had similar DFS (12-year DFS CMF = 67%; 12-year DFS CMF→ goserelin = 69%) compared with goserelin alone (12-year DFS = 61%, P= NS).
Conclusions: For pre/perimenopausal women with lymph-node-negative ER-positive breast cancer, CMF followed by goserelin improved DFS in comparison with either modality alone. The improvement was the most pronounced in those aged below 40, suggesting an endocrine effect of prolonged CMF-induced amenorrhea.
Keywords: amenorrhea, breast cancer, chemotherapy, goserelin, hormonal therapy, node negative