Breaking the matching in nested case–control data offered several advantages for risk estimation

Abstract 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.