2. Research Design and Methods
2.9 Methodological issues
2.9.3 Confounding
Confounding is defined as the mixing of effects of a third covariate with that of the exposure on disease, resulting in a biased effect estimate. In order for a covariate to be a confounder, it must be causally related to the exposure of interest and the outcome of interest (98). In this study, the main exposure in all analyses was a genotype or a haplotype.
Therefore, any confounders would have to causally affect a genotype or haplotype to meet the confounding criteria described by Rothman and Greenland. The effect of potential confounders was also evaluated using statistical models. If the addition of a covariate changed the |ln(OR)| of the exposure of interest by more than 0.10, then that covariate was considered a confounder.
While some associations between environmental variables and genotype may be observed due to the random error, no environmental variables were expected to be causally associated with candidate gene genotypes (meaning that on a directed acyclic graph (DAG), genotype was not the descendent of an environmental variable). Therefore even if the covariate was associated with genotype and outcome, it still did not meet the definition of a confounder. Even more importantly, if that covariate was on the causal pathway between genotype and the outcome, adjusting for that covariate could bias the association between exposure and outcome.
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candidate genotypes was ancestry. The proportion of African ancestry was estimated for each subject and included in all models, as described above. Adjustment for ancestry had little effect on ORs estimated for breast cancer overall and basal-like breast cancer. Ancestry adjustment did affect ORs for a small number of genotypes and luminal A breast cancer, and was adjusted for in all models to control for bias in these associations.
Effect measure modifiers in analyses of genotype-environment interactions can be susceptible to confounding by other environmental variables. However, this was problematic because adjusting for a confounder of the effect modifier has the potential to bias the
association of the genotype. Even more importantly, if the potential confounder was on the causal pathway between genotype and the outcome, adjusting for that covariate could drive the estimated genotype effect towards the null.
Relationships between WHR and basal-like and parity, lactation, and luminal A breast cancer were explored using DAGs. In the WHR-breast cancer DAG menopausal status and parity/lactation status were identified as potential confounders. Potential confounders from DAG analysis were evaluated for a statistical effect on the parameter estimate of
interest. Adjustment for these two risk factors did not alter the parameter estimates for WHR, and they were not included in further WHR interaction analyses. Reviews of the literature suggest that BMI is a confounder of the association between WHR and breast cancer in premenopausal women, and failure to adjust for BMI biases associations towards the null (82, 83). In CBCS data, BMI adjustment biased the association for between WHR and breast cancer overall by more than ln(OR) = 0.10. The bias for basal-like and luminal A
associations was lower than this threshold, but closer to 0.10 than to 0. Based on the effect of BMI adjustment in CBCS data and the acknowledgement of BMI as a confounder in the
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literature, BMI was kept as a confounder in WHR interaction models.
In the DAG examination of relationships between parity, lactation, and breast cancer, menopausal status and age at menarche were identified as potential confounders. Neither of these risk factors affected parameter estimates for the association between the combined parity and lactation variable and basal-like or luminal A breast cancer. Neither factor was included in further analyses as a confounder.
2.9.4 WHR misclassification
Waist and hip circumference were measured at the time of interview. Cases were interviewed a median 3.9 months (range, 0.8 – 42.5 months) after diagnosis, meaning that waist circumference and hip circumference may have been measured after the start of adjuvant therapy for some cases. If case WHR at the time of interview was systematically different from pre-diagnosis WHR, there is the potential for misclassification. There was no systematic event that would have led to WHR change in controls, so misclassification would be non-differential.
Weight change is a commonly documented side effect of breast cancer-related therapy [reviewed by (99, 100)]. In most patient series, patients gained approximately 2 to 20 pounds, and the amount of weight gained varied by study cohort and treatment (99, 100). Most studies reported that weight gain began shortly after breast cancer diagnosis, and the amount of weight gained increased over time (100-104). In some studies, patients experienced weight loss during the year following diagnosis (101, 105). Freedman et al. (106) reported that a group of healthy controls gained more weight on average than breast cancer patients receiving adjuvant therapy, but that the breast cancer patients had a greater fluctuation in weight during the time period shortly before the initiation of chemotherapy
136 until 6 months post-chemotherapy completion.
Ingram et al. (107) reported that post-diagnosis weight change was related to the type of adjuvant therapy, but other studies found no difference by chemotherapy type or regimen (103, 108, 109). Studies have also reported that weight change in breast cancer patients is associated with being premenopausal (99, 101, 106, 110). Two studies reported that lower pre-diagnosis BMI was associated with weight change, but another study did not find an association (101, 103, 109). There is also evidence that African-American breast cancer patients experienced greater weight gain compared to white patients, especially following adjuvant chemotherapy (101, 109).
In addition to changes in weight, studies have reported that breast cancer patients experienced increases in body fat percentage, fat mass, waist size, and hip size (102, 106, 111-113). Goodwin et al. (114) reported that although waist size and hip size increased 1 year after diagnosis, WHR was unchanged over the same time period. However women may have already started chemotherapy at the time of baseline WHR measurement, biasing the association toward the null.
A sensitivity analysis was conducted in order to estimate the potential effect that WHR misclassification due to chemotherapy might have on the association between WHR and basal-like breast cancer. The sensitivity analysis was conducted using a publicly available probabilistic bias analysis program <https://sites.google.com/site/biasanalysis/> (115), which calculates a simulated data table of “true” classification based on the observed data table and estimated sensitivity and specificity of the classification. The CBCS lacks data on whether cases had started chemotherapy by the time of interview. Sensitivity and specificity ranges were estimated based on the stage and race distribution in CBCS basal-like
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breast cancer cases and the prevalence of chemotherapy treatment by stage in North Carolina Central Cancer Registry data (116).
2.9.5 Outcome misclassification
Probabilistic sensitivity analyses were also conducted to evaluate the potential effects of molecular subtype misclassification. There has been some discussion in the literature as to the true definition of ‘basal-like’ breast cancer (117, 118). Not all studies use the same set of markers to define ‘basal-like’, and in studies that have used markers similar to those used by CBCS, there was not 100% agreement between tumors defined as basal-like using
microarray expression profiles and immunohistochemistry definitions (27, 119, 120).
Simulations of genotype and basal-like vs. luminal A associations were conducted, assuming non-differential misclassification of case status. Sensitivity and specificity ranges were based on previously published data (27, 119, 120). Sensitivity analyses were conducted using a publicly available program (115). All analyses were run for 5000 simulations.
2.10 Data interpretation
The results from this analysis were interpreted based on effect size, precision, and any trends or patterns in the data. The precision of the effect estimates were measured by
calculating the confidence interval ratio (CLR), which is equal to the upper 95% confidence limit divided by lower 95% confidence limit. A single CLR has relatively little meaning, but it can be useful for comparing several effect estimates to each other. A lower CLR indicates a more precise estimate. Null hypothesis testing was not used to draw conclusions about SNP or haplotype main effects.
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cancer overall (all cases) were reported here. The basal-like subtype is of interest because it is largely uncharacterized, and is a unique type of hormone-receptor negative breast cancer. The luminal A subtype is of interest because luminal A is the most common subtype and therefore a logical point of reference. Also, the candidate genes under study and potential effect measure modifiers were selected based on risk factors for these two subtypes. The luminal A and basal-like subtypes were also the two subtypes with the largest sample size. Even though parameters were estimated for luminal B, HER2+/ER-, and unclassified subtypes in the polytomous model, the associations were not reported due to limited sample size and imprecise OR estimates.
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