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Developing novel methods to study drug safety and effectiveness
514.934.1934 ext.44844
McGill University Health Centre
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 5 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 O13 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.
1-s2.0-S0895435616307582-main.pdf
File Size425.6 KiB
DateFebruary 27, 2017
Downloads546
AuthorBénédicte Delcoigne, Edoardo Colzani, Michaela Prochazka, Giovanna Gagliardi, Per Hall, Michal Abrahamowicz, Kamila Czene, Marie Reilly
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