Center for Observational Research, Amgen Inc., Thousand Oaks, CA 91320, USA.
Pharmacoepidemiol Drug Saf. 2013 Feb;22(2):111-21. doi: 10.1002/pds.3297. Epub 2012 Jun 4.
When medications are modified in response to changing clinical conditions, confounding by indication arises that cannot be controlled using traditional adjustment. Inverse probability of treatment weights (IPTWs) can address this confounding given assumptions of no unmeasured confounders and that all patients have a positive probability of receiving all levels of treatment (positivity). We sought to explore these assumptions empirically in the context of epoetin-alfa (EPO) dosing and mortality.
We developed a single set of IPTWs for seven EPO dose categories and evaluated achieved covariate balance, mortality hazard ratios, and confidence intervals using two levels of treatment model parameterization and weight deletion.
We found that IPTWs improved covariate balance for most confounders, but was not optimal for prior hemoglobin. Including more predictors in the treatment model or retaining highly weighted individuals resulted in estimates closer to the null, although precision decreased.
We chose to evaluate weights and covariate balance at a single time-point to facilitate an empirical analysis of model assumptions. These same assumptions are applicable to a time-dependent analysis, although empirical examination is not straight forward in that case. We find that the inclusion of rare treatment decisions and the high weights that result is needed for covariate balance under the positivity assumption. Removal of these influential weights can result in bias in either direction relative to the original confounding. It is therefore important to determine the reason for these rare patterns and whether inference is possible for all treatment levels.
当药物根据临床状况的变化而调整时，会出现混杂因素，传统的调整方法无法控制这种混杂因素。在没有未测量混杂因素和所有患者都有接受所有治疗水平的正概率（阳性）的假设下，逆概率治疗权重（IPTW）可以解决这种混杂问题。我们试图在促红细胞生成素-α（EPO）剂量和死亡率的背景下，从经验上探讨这些假设。
我们为七个 EPO 剂量类别开发了一组 IPTW，并使用两种治疗模型参数化和权重删除来评估实现的协变量平衡、死亡率风险比和置信区间。
我们发现，对于大多数混杂因素，IPTW 改善了协变量平衡，但对于先前的血红蛋白则不是最佳选择。在治疗模型中包含更多的预测因子或保留权重较高的个体，尽管精度降低，但会得到更接近零的估计值。
我们选择在单个时间点评估权重和协变量平衡，以便对模型假设进行实证分析。这些相同的假设适用于时变分析，尽管在这种情况下，实证检验并不直接。我们发现，在阳性假设下，需要包含罕见的治疗决策和由此产生的高权重，以实现协变量平衡。删除这些有影响力的权重可能会导致相对于原始混杂的偏差。因此，确定这些罕见模式的原因以及是否可以对所有治疗水平进行推断非常重要。
