title: 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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, SE-17177 Stockholm, Sweden. Electronic address: benedicte.delcoigne@ki.se.
authors.forename: Bénédicte
authors.initials: B
authors.lastname: Delcoigne
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, SE-17177 Stockholm, Sweden.
authors.forename: Edoardo
authors.initials: E
authors.lastname: Colzani
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, SE-17177 Stockholm, Sweden.
authors.forename: Michaela
authors.initials: M
authors.lastname: Prochazka
authors.affiliation: Section of Radiotherapy Physics and Engineering, Department of Medical Physics, Karolinska University Hospital, P.O. Box 260, Stockholm SE-17176, Sweden.
authors.forename: Giovanna
authors.initials: G
authors.lastname: Gagliardi
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, SE-17177 Stockholm, Sweden.
authors.forename: Per
authors.initials: P
authors.lastname: Hall
authors.affiliation: Department of Epidemiology and Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Avenue West, Montreal, Quebec H3A 1A1, Canada.
authors.forename: Michal
authors.initials: M
authors.lastname: Abrahamowicz
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, SE-17177 Stockholm, Sweden.
authors.forename: Kamila
authors.initials: K
authors.lastname: Czene
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, SE-17177 Stockholm, Sweden.
authors.forename: Marie
authors.initials: M
authors.lastname: Reilly
mesh_terms: D000368:Aged; D001943:Breast Neoplasms / Q000453:epidemiology* / Q000532:radiotherapy; D016022:Case-Control Studies; D015984:Causality; D015897:Comorbidity; D005260:Female; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000453:epidemiology* / Q000532:radiotherapy; D008875:Middle Aged; D016016:Proportional Hazards Models; D012306:Risk; D012307:Risk Factors; D012907:Smoking / Q000453:epidemiology; D013548:Sweden / Q000453:epidemiology
keywords: Absolute risk; Cumulative incidence; Efficiency; Inverse probability weighting; Weighted Cox regression; Weighted likelihood