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This study introduces an adaptive Bayesian monitoring method for designing validation substudies. It efficiently determines if enough data are collected for accurate predictive value estimation in epidemiological studies.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Research Methods

Background:

  • Internal validation substudies compare imperfect measurements to gold standards.
  • Existing guidance assumes complete cohort enrollment, lacking methods for ongoing data collection.
  • Designing validation substudies during active cohort data collection presents challenges.

Purpose of the Study:

  • To develop an adaptive approach for validation substudy design using Bayesian monitoring.
  • To establish criteria for determining sufficient validation data for estimating positive and negative predictive values.
  • To provide a flexible method applicable to various epidemiologic studies.

Main Methods:

  • Utilized Bayesian monitoring frameworks for adaptive study design.
  • Developed criteria to assess the sufficiency of validation data for predictive value estimation.
  • Applied the method to the Study of Transition, Outcomes and Gender (STOG).

Main Results:

  • Demonstrated the method's ability to determine the efficacy of validation data collection.
  • Showcased the method's capacity to identify futility when a mismeasured variable is an inadequate substitute for a gold standard.
  • Validated the approach using a cohort study of transgender and gender nonconforming individuals.

Conclusions:

  • The proposed adaptive method offers an effective and efficient approach to estimating classification parameters.
  • This novel method allows for real-time assessment of validation data adequacy.
  • The approach is adaptable to different epidemiologic study designs and objectives.