存在时依性混杂时的G方法

G-methods in the existence of time varying confounding

目的介绍处理时依性混杂的G方法,并对不同G方法进行探讨和比较。方法通过4个情境的模拟试验验证不同G方法在不同情境下对时依性混杂的处理效果,并应用英国生物样本库(UK Biobank)的数据集进行实例分析。结果模拟试验和实例分析结果均显示G方法能有效处理时依性混杂。模拟试验显示3种方法效果类似,G-computation易受G-null paradox的影响。随着时依性混杂因素数量增加,相比于G-computation和G-estimation,逆概率加权法(inverse probability of treatment weighting,IPTW)的效果波动较大。结论不同G方法都能适当地处理时依性混杂,降低统计分析过程中的偏倚大小。

Objective To introduce and compare different G-methods which can deal with time varying confounding.Methods The simulation experiments of four scenarios were carried out to verify the effects of different G-methods on time varying confounding in different situations.Dataset from UK Biobank was then analyzed using different G-methods.Results All three G methods can effectively deal with time varying confounding with similar performance,while G-computation was vulnerable to G-null paradox.However,with the increasing number of time varying confounders,the estimated effects of inverse probability of treatment weighting(IPTW)were more variable.Conclusion All of the three Gmethods can remove the bias resulted from time varying confounding appropriately.