270 variables, as this is a conservative choice based on observed values. We performed data analysis using age-matched condi- tional logistic regression as in our prior study of DDT and breast cancer in this same population [25]. We began with a full model, entering all PCBs classiﬁed by Wolff et al. [24]. as potentially relevant to human health: Group 1 consisted of congeners detected in the CHDS serum sam- ples that were considered to be potentially estrogenic and persistent (PCB 101,187, 201). Group 2 consisted of congeners detected in the CHDS serum samples that were considered to be potentially antiestrogenic, immunotoxic, dioxin-like: Group 2A which are non-ortho or mono-ortho in their structure (PCB 66, 74,105,118,156,167) and Group 2B which are di-ortho and have more limited dioxin-like activity (PCB 138 and 170). Group 3 consisted of pheno- barbital, CYP1A and CYP2B inducers (PCB 99, 153,180, 183, 203). Congeners that had individual p [ 0.20 were tested for removal as a group, based on a likelihood ratio test (p \ 0.15 as the criterion). At the next step, we used a more stringent criterion, eliminating remaining PCB terms with individual signiﬁcance probabilities [0.05. In addi- tion to using the likelihood ratio test to test the hypothesis that the coefﬁcients for these terms were each 0 using the criterion, p \ 0.15, we also examined the sign and size of coefﬁcients of remaining predictors before and after elim- ination to rule out major confounding by the eliminated predictors. Our goal was to identify the minimal number of PCBs that predicted risk. Once the best PCB model was identiﬁed, we examined whether further adjustment for blood lipids (total cholesterol, total triglycerides), parity, year of blood draw, body mass index (lower tertile, upper tertile vs. middle tertile as the reference category) and breast feeding following the current pregnancy altered PCB associations with breast cancer. To describe the net effect of PCB exposure on breast cancer, we constructed a post-hoc score that consisted of the ratio of the sum of PCB congener(s) associated with higher risk of breast cancer to the sum PCB congeners associated with lower risk as in a previous report on health effects of PCB exposure in this population [26]. We then examined the variation in the post-hoc score and described its association with breast cancer in this population. Results 123 Table 1 shows the distribution of PCB congeners and a post-hoc PCB score based on the ﬁnal model shown in Table 2. In this study population, PCB congener concen- trations and the distribution of the PCB score were highly variable. The comparisons between cases and controls in Table 1 show no remarkable, statistically signiﬁcant Breast Cancer Res Treat (2012) 136:267–275 Table 2 Associations of individual PCB congeners with breast can- cer diagnosed before 50 years of age PCB Model with all PCBs Final model Coefﬁcient p value Coefﬁcient p value Classiﬁcation [24] Estrogenic Anti-estrogenic A. Non- ortho, mono- ortho, dioxin- like B. Di-ortho, limited dioxin activity Phenobarbital, CYP1A, and CYP2B inducers 101 187 201 66 74 105 118 156 167 138 170 99 153 180 183 203 0.39 -4.18 -2.70 0.11 0.36 1.37 -0.82 -0.42 -2.50 0.10 -1.45 -0.45 0.10 1.26 1.15 6.17 0.41 0.03 0.23 0.61 0.53 0.19 0.07 0.61 0.17 0.87 0.34 0.56 0.91 0.28 0.48 0.01 -2.07 0.02 -2.88 0.03 4.36 0.001 In the column labeled, ‘‘Model with all PCBs’’, congeners were entered into a single model as the ﬁrst step in model selection. Congeners PCB 203, 187, 167 118, and 105 were initially retained based on the criterion of p \ 0.20. PCB 118 and 105 were then removed either one at a time or together. They were deleted from the ﬁnal model by applying the likelihood ratio test where v2 = 3.81, 2df, p = 0.15 for the test to retain both PCB 105 and PCB 118 in the model. Both were tested for removal simultaneously because deleting either PCB 118 or PCB 105 alone greatly affected the size of coef- ﬁcient for the other. There were N = 112 age-matched case–control pairs for all models shown and all models tested. Coefﬁcients are reported per 1 mmol/l associations between single PCB congeners and breast cancer risk, in the absence of control for other congeners. This observation suggests that confounding would play a role in any associations observed in multivariate models. There were no associations between the sum of total PCBs or with PCB groups (groups shown in Table 1) and risk of breast cancer (data not shown). Table 2 shows results of multivariate conditional logistic regression models for individual PCB congeners. PCB 203 is the only congener with a consistent and statistically signiﬁcant positive coefﬁcient, indicating that it was associated with increased risk of breast cancer (Table 2, both models). In contrast, PCB 167 and PCB 187 were inversely associated with risk of breast cancer (Table 2, both models). To check for dose response, we also estimated associations for these PCBs by quartile shown in Table 3. Results were largely consistent with a monotonic trend for each congener.