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Differential Misclassification by Time: A Proposed Validation Substudy Design to Account for Time Trends in Bias
Katherine A Lawson-Michod1, Julie Barberio2, Richard F MacLehose3
1From the Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT.
Epidemiology (Cambridge, Mass.)
|May 18, 2026
Summary
Validation substudies can account for time trends in classification parameters. Purposeful sampling accurately estimates changing predictive values over time, improving bias analysis in long-term studies.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Validation substudies quantify bias from misclassification by estimating classification parameters.
- Longitudinal studies may experience time-varying classification parameters, impacting bias analysis and validation substudy design.
Purpose of the Study:
- To provide guidance for sampling validation data to address time trends in classification parameters.
- To evaluate methods for estimating time-varying bias in epidemiological studies.
Main Methods:
- Simulated a cohort of 10,000 observations with linear and logarithmic time trends in exposure misclassification.
- Conducted validation sampling at multiple time points (beginning, middle, end of follow-up).
- Imputed time-varying positive and negative predictive values and compared them to true values.
Main Results:
- Purposeful sampling effectively estimates changing predictive values in the presence of time trends.
- Accurate estimation of predictive values was achieved under linear and logarithmic time trend scenarios.
- Higher accuracy was observed when a larger proportion of exposure/outcome strata was sampled.
Conclusions:
- Designs estimating time-varying predictive values are superior to conventional designs when classification parameters change over time.
- Accounting for time trends in classification parameters is crucial for accurate bias analysis in longitudinal studies.
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