van der Wal Willem M, Noordzij Marlies, Dekker Friedo W, Boeschoten Elisabeth W, Krediet Raymond T, Korevaar Johanna C, Geskus Ronald B
University of Amsterdam, the Netherlands.
Int J Biostat. 2010;6(1):Article 2. doi: 10.2202/1557-4679.1166.
When comparing the causal effect of peritoneal dialysis (PD) and hemodialysis (HD) treatment on lowering mortality in renal patients, using observational data, it is necessary to adjust for different forms of confounding and informative censoring. Both the type of dialysis treatment that is started with and mortality are affected by baseline covariates. Longitudinal and baseline variables can affect both the probability of switching from one type of dialysis to the other, and mortality. Longitudinal and baseline variables can also affect the probability of receiving a kidney transplant, possibly causing informative censoring. Adjusting for longitudinal variables by including them as covariates in a regression model potentially causes bias, for instance by losing a possible indirect effect of dialysis on mortality via these longitudinal variables. Instead, we fitted a marginal structural model (MSM) to estimate the causal effect of dialysis type, adjusted for confounding and informative censoring. We used the MSM to compare the hazard of death as well as cumulative survival between the potential treatment trajectories "always PD" and "always HD" over time, conditional on age and diabetes mellitus status. We used inverse probability weighting (IPW) to fit the MSM.
在使用观察性数据比较腹膜透析（PD）和血液透析（HD）治疗对降低肾病患者死亡率的因果效应时，有必要对不同形式的混杂因素和信息性删失进行调整。开始时采用的透析治疗类型和死亡率均受基线协变量的影响。纵向变量和基线变量既会影响从一种透析类型转换为另一种透析类型的概率，也会影响死亡率。纵向变量和基线变量还会影响接受肾移植的概率，可能导致信息性删失。通过将纵向变量作为协变量纳入回归模型来对其进行调整，可能会导致偏差，例如可能会遗漏透析通过这些纵向变量对死亡率产生的间接影响。相反，我们拟合了一个边际结构模型（MSM）来估计透析类型的因果效应，并对混杂因素和信息性删失进行了调整。我们使用MSM来比较潜在治疗轨迹“始终采用PD”和“始终采用HD”随时间推移的死亡风险以及累积生存率，并以年龄和糖尿病状态为条件。我们使用逆概率加权（IPW）来拟合MSM。
