Comparing methods of misclassification correction for studies of adolescent alcohol use

Despite concerns over measurement error, self-report continues to be the most common measure of adolescent alcohol use used by researchers. Objective measures of adolescent alcohol use continue to advance; however, they tend to be cost prohibitive for larger studies. By combining appropriate statistical techniques and validation subsamples, the benefits of objective alcohol measures can be made more accessible to a greater number of researchers.

To compare three easily implemented methods to correct for measurement error when objective measures of alcohol use are available for a subsample of participants, regression calibration, multiple imputation for measurement error (MIME), and probabilistic sensitivity analysis (PSA), and provide guidance regarding the use of each method in scenarios likely to occur in practice.

This simulation experiment compared the performance of each method across different sample sizes, both differential and non-differential error, and differing levels of sensitivity and specificity of the exposure measure.

Failure to adjust for measurement error led to substantial bias across all simulated scenarios ranging from a 35% to 208% change in the log-odds. For non-differential misclassification, regression calibration reduced this bias to between a 1% and 23% change in the log-odds regardless of sample size. At higher sample sizes, MIME produced approximately unbiased (between a 0% and 9% change in the log-odds) and relatively efficient corrections for both non-differential and differential misclassification. PSA provided little utility for correcting misclassification due to the inefficiency of its estimates.

Concern over measurement error resulting from self-reported adolescent alcohol use persists in research. Where appropriate, methods involving validity subsamples provide an efficient avenue for addressing these concerns.