B1257
Title: Semi-parametric estimation of biomarker age trends with endogenous medication use in longitudinal data
Authors: Andrew Spieker - Vanderbilt University Medical Center (United States) [presenting]
Joseph Delaney - University of Manitoba (Canada)
Robyn McClelland - University of Washington (United States)
Abstract: In cohort studies, non-random medication use can pose barriers to the estimation of the natural history trend in a mean biomarker value (namely, the association between a predictor of interest and a biomarker outcome that would be observed in the total absence of biomarker-specific treatment). Common causes of treatment and outcomes are often unmeasured, obscuring our ability to easily account for medication use with assumptions commonly invoked in causal inference such as conditional ignorability. Without confidence in the availability of a variable satisfying the exclusion restriction, the use of instrumental variable approaches may be difficult to justify. Heckman's hybrid model with structural shift can be used to correct endogeneity bias via a homogeneity assumption and parametric specification of a joint model for the outcome and treatment. The application of this methodology to settings of longitudinal data remains unexplored. We demonstrate how the assumptions of the treatment effects model can be extended to accommodate clustered data arising from longitudinal studies. The proposed approach is semi-parametric in nature in that valid inference can be obtained without the need to specify any component of the longitudinal correlation structure and can serve as a useful tool to uncover natural history trends in longitudinal data that are obscured by endogenous treatment.
