Evaluation and comparison of covariate balance metrics in studies with time-dependent confounding
arxiv(2024)
摘要
Marginal structural models have been increasingly used by analysts in recent
years to account for confounding bias in studies with time-varying treatments.
The parameters of these models are often estimated using inverse probability of
treatment weighting. To ensure that the estimated weights adequately control
confounding, it is possible to check for residual imbalance between treatment
groups in the weighted data. Several balance metrics have been developed and
compared in the cross-sectional case but have not yet been evaluated and
compared in longitudinal studies with time-varying treatment. We have first
extended the definition of several balance metrics to the case of a
time-varying treatment, with or without censoring. We then compared the
performance of these balance metrics in a simulation study by assessing the
strength of the association between their estimated level of imbalance and
bias. We found that the Mahalanobis balance performed best.Finally, the method
was illustrated for estimating the cumulative effect of statins exposure over
one year on the risk of cardiovascular disease or death in people aged 65 and
over in population-wide administrative data. This illustration confirms the
feasibility of employing our proposed metrics in large databases with multiple
time-points.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要