Application of the Adaptive Validation Design to estimate the association between transmasculine/transfeminine status and self-inflicted injury among transgender and gender-nonconforming children and adolescents.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSAn 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, expected to equal the association that would have been observed had the gold standard measurement been available for the entire study population. Guidance on optimal sampling of participants to include in validation substudies has not considered monitoring validation data as they accrue. In this paper, we develop and apply the framework of Bayesian monitoring to determine when sufficient validation data have been collected to yield a bias-adjusted estimate of association with a prespecified level of precision. MethodsWe demonstrate the utility of this method using the Study of Transition, Outcomes and Gender--a cohort study of transgender and gender non-conforming children and adolescents. Transmasculine and transfeminine status were determined from the gender code in the electronic medical record at cohort enrollment. This status is known to be misclassified because it can indicate either gender identity or sex recorded at birth. Our interest is in the association between transmasculine and transfeminine status and self-inflicted injury. To address possible exposure misclassification, we demonstrate the methods ability to determine when sufficient validation data have been collected to calculate a bias-adjusted estimate of association that is less than 80% greater than the precision of the conventional estimate. ResultsIn the conventional age-adjusted analysis, we observed that transmasculine children and adolescents were 1.80-fold more likely to inflict self-harm than transfeminine youths (95%CI 1.27, 2.55). Using the adaptive validation approach, 200 cohort members were required for validation to yield a bias-adjusted estimate of OR=3.03 (95%CI 1.76, 5.56), which was similar to the bias-adjusted estimate using complete validation data (OR=2.63, 95%CI 1.67, 4.23). ConclusionsOur method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue. This 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.