OBJECTIVE: To demonstrate the advantage of using weighted Cox regression to analyze nested case-control data in overcoming limitations encountered with traditional conditional logistic regression.STUDY DESIGN AND SETTING: We analyzed data from 1,051 women who were sampled in a case-control study of lung cancer nested within a cohort of breast cancer patients. We investigated how lung cancer risk is associated with radiation therapy and modified by smoking, with both conditional logistic regression and weighted Cox regression models.RESULTS: In contrast to logistic regression, weighted Cox regression exploited the information regarding radiation dose received by each individual lung. The weighted method also mitigated a problem of overmatching apparent in the data and revealed that the risk of radiotherapy-associated lung cancer was modified by smoking (P = 0.026) with a hazard ratio of 4.09 (2.31, 7.24) in unexposed smokers and 8.63 (5.04, 14.79) in smokers receiving doses >13 Gy. The cumulative risk of lung cancer increased steadily with increasing radiotherapy dose in smokers, whereas no such effect was found in nonsmokers.CONCLUSION: The weighted Cox regression makes optimal and versatile use of the information in a nested case-control design, allowing dose-response analysis of exposure to paired organs and enabling the estimation of cumulative risk.
Aged Breast Neoplasms