In studying the marginal effect of antidepressants on body mass index using electronic health records data, we face several challenges. Patients' characteristics can affect the exposure (confounding) as well as the timing of routine visits (measurement process), and those characteristics may be altered following a visit which can create dependencies between the monitoring and body mass index when viewed as a stochastic or random processes in time. This may result in a form of selection bias that distorts the estimation of the marginal effect of the antidepressant. Inverse intensity of visit weights have been proposed to adjust for these imbalances, however no approaches have addressed complex settings where the covariate and the monitoring processes affect each other in time so as to induce endogeneity, a situation likely to occur in electronic health records. We review how selection bias due to outcome-dependent follow-up times may arise and propose a new cumulated weight that models a complete monitoring path so as to address the above-mentioned challenges and produce a reliable estimate of the impact of antidepressants on body mass index. More specifically, we do so using data from the Clinical Practice Research Datalink in the United Kingdom, comparing the marginal effect of two commonly used antidepressants, citalopram and fluoxetine, on body mass index. The results are compared to those obtained with simpler methods that do not account for the extent of the dependence due to an endogenous covariate process.
翻译:在利用电子健康记录数据研究抗抑郁剂对身体质量指数的边际影响时,我们面临若干挑战:病人的特征可能影响接触(诊断)和定期访问的时间(测量过程),这些特征在访问后可能会改变,因为访问可能导致监测与身体质量指数之间产生依赖性,如果认为是随机或随机过程,则这种依赖性在监测与身体质量指数之间产生依赖性;这可能导致一种选择偏差,扭曲对抗抑郁剂的边际影响的估计;为调整这些不平衡提出了访问权重的反常度的反常度,但更具体地说,我们没有办法处理共变率和监测过程在时间上相互影响的复杂环境,以便诱发异性,在电子健康记录中可能出现这种情况;我们审查由于依赖结果的后续时间或随机过程而可能产生的选择偏差,并提出新的累积权重,以模拟完整的监测路径,从而应对上述挑战,并对抗抑郁剂对身体质量指数的影响作出可靠的估计;更具体地说,我们没有采用从临床做法和监测过程中产生的数据,因此,将内生性研究程度的数据与内流数据联系与共同采用的较简单的机构对比。