The dose model was constructed to reduce nondifferential exposure misclassification due to variations in personal behavior. In the RDD analysis, exposure was based solely on subjects' RDD values and did not take into consideration factors such as bathing habits and bottled water consumption. Nondifferential exposure misclassification should bias results towards the null when the exposure is dichotomous. Based on this reasoning, we expected the moderate elevations in risk observed in the RDD analysis by Aschengrau et al. [5
] to increase further in the current PDD analysis. The results show that, in general, this was not the case.
Overall, the risks calculated from the PDD analysis differed only slightly from the RDD analysis, if at all. The fact that the PDD model did not increase the odds ratios may be due to a number of reasons. A possible explanation is that no association exists between exposure to PCE and breast cancer, but there is a fairly large body of literature now that supports a carcinogenic effect for PCE in humans. The biologic rationale for a breast cancer effect stems from a hypothesis described by Labreche and Goldberg that organic solvents such as PCE may act either directly as genotoxic agents or indirectly through their metabolites to increase the risk of breast cancer [15
More likely, the impact of variations in personal habits was small in comparison to variations in characteristics of the drinking water distribution system, or the questionnaire information did not accurately account for individual variations. Errors in estimating the RDD values used in the dose model may explain why the model made little difference in determining risk. Improper assumptions or incorrect input variables in the Webler-Brown model led to errors in the RDD values [5
]. The resulting exposure misclassification would not be corrected using the dose model. As a result, the dose model would still be biased.
Furthermore, both RDD and PDD are measures of cumulative exposure, where exposure was summed over a subject's residences on Cape Cod. One subject may have been exposed at a high intensity for two or more short residency durations while another subject with the same exposure value may have been exposed at a low intensity for one long residency duration. The exposure pattern can influence cancer risk if, for example, a threshold intensity of PCE must be reached in order to cause breast cancer or if breast cancer induction requires prolonged continuous exposure [16
Another limitation of the analysis was the restriction to subjects with non-proxy interviews, which reduced the sample size by 31%. When all subjects were included in the RDD analysis, small to moderate increases were observed among women whose exposure level was greater than the 90th
]. When only non-proxy subjects were included, we no longer observed moderate increases. This difference may be due to the fact that the maximum RDD value was higher for all subjects than for non-proxies. Therefore, the use of only non-proxy subjects may not accurately reflect population risk. Imputing values for proxy subjects is a possible option for future analyses.
Faulty recall in the behavioral data is another possible reason why the PDD model did not strengthen the association between breast cancer and PCE. Subjects were asked to remember details about bathing habits and drinking water that occurred up to forty years before the interview. As a result, the exposure data obtained at interview may not be accurate.
The inputs that most heavily influenced the PDD model were initial water concentration and duration of exposure. These variables were also included in the RDD model. In this study population, personal factors like bath and shower temperature, bathing frequencies and durations, and water consumption did not differ greatly among subjects. Therefore, including these characteristics in the PDD model did not significantly improve the exposure measure or change which subjects were considered exposed and to what level they were exposed.