Adaptive Validation Design: A Bayesian Approach to Validation Substudy Design with Prospective Data Collection

Details

• Personal Author:
  Ahern TP ; Collin LJ ; Getahun D ; Goodman M ; Lash TL ; MacLehose RF ; Nash R ; Roblin D ; Silverberg MJ ; Ahern TP ; Collin LJ ; Getahun D ; Goodman M ; Lash TL ; MacLehose RF ; Nash R ; Roblin D ; Silverberg MJ
• Description:
  An internal validation substudy compares an imperfect measurement of a variable with a gold-standard measurement in a subset of the study population. Validation data permit calculation of a bias-adjusted estimate, which has the same expected value as the association that would have been observed had the gold-standard measurement been available for the entire study population. Existing guidance on optimal sampling for validation substudies assumes complete enrollment and follow-up of the target cohort. No guidance exists for validation substudy design while cohort data are actively being collected. In this article, we use the framework of Bayesian monitoring methods to develop an adaptive approach to validation study design. This method monitors whether sufficient validation data have been collected to meet predefined criteria for estimation of the positive and negative predictive values. We demonstrate the utility of this method using the Study of Transition, Outcomes and Gender-a cohort study of transgender and gender nonconforming people. We demonstrate the method's ability to determine efficacy (when sufficient validation data have accumulated to obtain estimates of the predictive values that fall above a threshold value) and futility (when sufficient validation data have accumulated to conclude the mismeasured variable is an untenable substitute for the gold-standard measurement). This proposed method can be applied within the context of any parent epidemiologic study design and modified to meet alternative criteria given specific study or validation study objectives. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue. [Description provided by NIOSH]
• Subjects:
  Cohort Studies Epidemiology Occupational Health Safety
• Keywords:
  Analytical Models Author Keywords: Bayesian Methods Cohort Studies Mathematical Models Statistical Analysis Statistical Quality Control Validation Study Design
• ISSN:
  1044-3983
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article ; Journal Article
• Place as Subject:
  California ; Georgia ; Maryland ; Minnesota ; OSHA Region 1 ; OSHA Region 3 ; OSHA Region 4 ; OSHA Region 5 ; OSHA Region 9 ; Vermont
• CIO:
  National Institute for Occupational Safety and Health (NIOSH) ; National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health ; Workplace Safety & Health
• Location:
  North America ; North America
• Volume:
  31
• Issue:
  4
• NIOSHTIC Number:
  nn:20064480
• Citation:
  Epidemiology 2020 Jul; 31(4):509-516
• Contact Point Address:
  Lindsay J. Collin, Department of Epidemiology, Emory University, 1518 Clifton Rd NE Atlanta, GA 30322
• Email:
  lindsay.jane.collin@emory.edu
• Federal Fiscal Year:
  2020
• Performing Organization:
  University of Minnesota Twin Cities
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Epidemiology
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:11129459c1567ad0c599c38c323a7a6a40aa7e3ac76db57f2369405f899c0abb37d4a32cc4169ecde4669ca1e5047ffa7ffa6923af9db6685db9b656f03bb930
• Download URL:
  https://stacks.cdc.gov/view/cdc/222932/cdc_222932_DS1.pdf
• File Type:
  [PDF - 16.85 MB ]