Evaluating the treatment effects model for estimation of cross-sectional associations between risk factors and cardiovascular biomarkers influenced by medication use

摘要

Purpose In cross-sectional observational data, evaluation of biomarker-to-exposure associations is often complicated by nonrandom medication use. Traditional approaches often lead to biased estimates, consistent with known results involving confounding by indication. More sophisticated, yet easy to implement approaches such as inverse probability weighting and censored normal regression can address medication use in certain settings but have poor performance when medication use depends on off-medication biomarker values. More sophisticated approaches are necessary.