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author:("wetzel, C.")
1.  The impact of obesity on receipt of adjuvant chemotherapy for breast cancer in the National Comprehensive Cancer Network (NCCN) centers 
Disparities in the receipt of adjuvant chemotherapy for early stage breast cancer is an important factor influencing mortality. We investigated whether greater body mass index (BMI) decreases receipt of adjuvant chemotherapy among women with operable breast cancer. In the NCCN breast cancer outcomes database, we identified women age ≤70 with newly diagnosed stage I, II or III breast cancer between 1997 and 2007, for whom use of adjuvant chemotherapy was classified as either standard-of-care or discretionary based on their clinical characteristics. Body mass index was assessed in categories (<18.5 kg/m2 [underweight], 18.5 to <25 kg/m2 [normal], 25 to <30 kg/m2 [overweight], 30 to 39kg/m2 [obese], ≥40 kg/m2 [extreme obese]). Multivariable logistic regression analysis was used to examine the association between BMI and receipt of chemotherapy in each classification group. 9,527 women were eligible for the study; 40% normal weight or less; 31% overweight; 24% obese; and 5% extremely obese. In multivariable analysis, there was no significant association between BMI and receipt of chemotherapy in either classification group. Among women for whom chemotherapy would be considered standard-of-care, older age (p<.001), comorbidity (p<.001), and non-Hispanic black ethnicity (p=.002) were associated with a lower likelihood of receipt of chemotherapy; however, the effect of ethnicity was not mediated by obesity. Among women treated for operable breast cancer in the NCCN centers, BMI had no impact on receipt of adjuvant chemotherapy and did explain the lower likelihood of chemotherapy among non-Hispanic black patients. Further investigation is needed into other factors that contribute to patient disparities in the receipt of chemotherapy in major academic centers.
PMCID: PMC3436594  PMID: 21809116
Obesity; Breast Neoplasms; Chemotherapy; Adjuvant; Standard of Care; Healthcare Disparities
2.  Evaluating a New Risk Marker’s Predictive Contribution in Survival Models 
Although the area under the receiver operating characteristic (ROC) curve (AUC) is the most popular measure of the performance of prediction models, it has limitations, especially when it is used to evaluate the added discrimination of a new risk marker in an existing risk model. Pencina et al. (2008) proposed two indices, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI), to supplement the improvement in the AUC (IAUC). Their NRI and IDI are based on binary outcomes in case-control settings, which do not involve time-to-event outcome. However, many disease outcomes are time-dependent and the onset time can be censored. Measuring discrimination potential of a prognostic marker without considering time to event can lead to biased estimates. In this paper, we extended the NRI and IDI to time-to-event settings and derived the corresponding sample estimators and asymptotic tests. Simulation studies showed that the time-dependent NRI and IDI have better performance than Pencina’s NRI and IDI for measuring the improved discriminatory power of a new risk marker in prognostic survival models.
PMCID: PMC3439820  PMID: 22984361
Improved discrimination; Prognostic survival models; Time-dependent NRI; Time-dependent IDI
3.  Comparison of discriminatory power and accuracy of three lung cancer risk models 
British Journal of Cancer  2010;103(3):423-429.
Three lung cancer (LC) models have recently been constructed to predict an individual's absolute risk of LC within a defined period. Given their potential application in prevention strategies, a comparison of their accuracy in an independent population is important.
We used data for 3197 patients with LC and 1703 cancer-free controls recruited to an ongoing case–control study at the Harvard School of Public Health and Massachusetts General Hospital. We estimated the 5-year LC risk for each risk model and compared the discriminatory power, accuracy, and clinical utility of these models.
Overall, the Liverpool Lung Project (LLP) and Spitz models had comparable discriminatory power (0.69), whereas the Bach model had significantly lower power (0.66; P=0.02). Positive predictive values were highest with the Spitz models, whereas negative predictive values were highest with the LLP model. The Spitz and Bach models had lower sensitivity but better specificity than did the LLP model.
We observed modest differences in discriminatory power among the three LC risk models, but discriminatory powers were moderate at best, highlighting the difficulty in developing effective risk models.
PMCID: PMC2920015  PMID: 20588271
lung cancer; risk model; 5-year lung cancer risk; relative risks; discriminatory power

Results 1-4 (4)