Breaking the matching in nested case-control data offered several advantages for risk estimation.
J Clin Epidemiol
; 82: 79-86, 2017 Feb.
Article
em En
| MEDLINE
| ID: mdl-27923734
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 ANDSETTING:
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Neoplasias Pulmonares
Tipo de estudo:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Aged
/
Female
/
Humans
/
Middle aged
País/Região como assunto:
Europa
Idioma:
En
Revista:
J Clin Epidemiol
Assunto da revista:
EPIDEMIOLOGIA
Ano de publicação:
2017
Tipo de documento:
Article