The parametric g-formula for time-to-event data: intuition and a worked example

Details

• Personal Author:
  Cole SR ; Edwards JK ; Keil AP ; Naimi AI ; Richardson DB
• Description:
  BACKGROUND: The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied. METHODS: We provide a simple introduction to the parametric g-formula and illustrate its application in an analysis of a small cohort study of bone marrow transplant patients in which the effect of treatment on mortality is subject to time-varying confounding. RESULTS: Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the g-formula. CONCLUSIONS: The g-formula allows estimation of a relevant parameter for public health officials: the change in the hazard of mortality under a hypothetical intervention, such as reduction of exposure to a harmful agent or introduction of a beneficial new treatment. We present a simple approach to implement the parametric g-formula that is sufficiently general to allow easy adaptation to many settings of public health relevance. [Description provided by NIOSH]
• Subjects:
  Environmental Monitoring Epidemiology Mortality Musculoskeletal System Occupational Health Public Health Risk Assessment Safety
• Keywords:
  Adaptation Analytical-methods Analytical-processes Bone-marrow Exposure-assessment Mathematical-models Medical-treatment Mortality-data Mortality-rates Public-health Risk-analysis Statistical-analysis

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• ISSN:
  1044-3983
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  North Carolina ; OSHA Region 4
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  25
• Issue:
  6
• NIOSHTIC Number:
  nn:20046294
• Citation:
  Epidemiology 2014 Nov; 25(6):889-897
• Contact Point Address:
  Alexander P. Keil, Department of Epidemiology, CB 7435, University of North Carolina, Chapel Hill, NC 27599-7435
• Email:
  akeil@unc.edu
• Federal Fiscal Year:
  2015
• Performing Organization:
  University of North Carolina, Chapel Hill
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Epidemiology
• End Date:
  20270630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0b164faae404cddaeb9c8b0017654780a409f21e10a889066199920d87f0798c57888f297eed36f1fcf6991d29da1396d0f249d6165126c94e08e90ef2498a0b
• Download URL:
  https://stacks.cdc.gov/view/cdc/201397/cdc_201397_DS1.pdf
• File Type:
  [PDF - 1.16 MB ]