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Risk Ratio and Risk Difference Estimation in Case-cohort Studies

Journal Article · · Journal of Epidemiology
 [1];  [2];  [3]
  1. The Institute of Statistical Mathematics, Tokyo (Japan)
  2. Radiation Effects Research Foundation, Hiroshima (Japan)
  3. Kyoto University (Japan)
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Background: In case-cohort studies with binary outcomes, ordinary logistic regression analyses have been widely used because of their computational simplicity. However, the resultant odds ratio estimates cannot be interpreted as relative risk measures unless the event rate is low. The risk ratio and risk difference are more favorable outcome measures that are directly interpreted as effect measures without the rare disease assumption. Methods: We provide pseudo-Poisson and pseudo-normal linear regression methods for estimating risk ratios and risk differences in analyses of case-cohort studies. These multivariate regression models are fitted by weighting the inverses of sampling probabilities. Also, the precisions of the risk ratio and risk difference estimators can be improved using auxiliary variable information, specifically by adapting the calibrated or estimated weights, which are readily measured on all samples from the whole cohort. Finally, we provide computational code in R (R Foundation for Statistical Computing, Vienna, Austria) that can easily perform these methods. Results: Through numerical analyses of artificially simulated data and the National Wilms Tumor Study data, accurate risk ratio and risk difference estimates were obtained using the pseudo-Poisson and pseudo-normal linear regression methods. Also, using the auxiliary variable information from the whole cohort, precisions of these estimators were markedly improved. Conclusion: The ordinary logistic regression analyses may provide uninterpretable effect measure estimates, and the risk ratio and risk difference estimation methods are effective alternative approaches for case-cohort studies. These methods are especially recommended under situations in which the event rate is not low.
Research Organization:
Radiation Effects Research Foundation (RERF), Hiroshima (Japan)
Sponsoring Organization:
Japan Society for the Promotion of Science; USDOE
OSTI ID:
2470387
Journal Information:
Journal of Epidemiology, Journal Name: Journal of Epidemiology Journal Issue: 10 Vol. 33; ISSN 0917-5040
Publisher:
Japan Epidemiological Association (JEA)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

60 APPLIED LIFE SCIENCES
calibration estimator
case-cohort study
effect measure
public
environmental & occupational health
rare disease assumption
two-phase epidemiological design