Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academisch Medisch Centrum, University of Amsterdam, Amsterdam, The Netherlands.
Stat Med. 2022 Sep 20;41(21):4176-4199. doi: 10.1002/sim.9503. Epub 2022 Jul 9.
When drawing causal inference from observed data, failure time outcomes present additional challenges of censoring often combined with other missing data patterns. In this article, we follow incident cases of end-stage renal disease to examine the effect on all-cause mortality of starting treatment with transplant, so-called pre-emptive kidney transplantation, vs starting with dialysis possibly followed by delayed transplantation. The question is relatively simple: which start-off treatment is expected to bring the best survival for a target population? To address it, we emulate a target trial drawing on the long term Swedish Renal Registry, where a growing common set of baseline covariates was measured nationwide. Several lessons are learned which pertain to long term disease registers more generally. With characteristics of cases and versions of treatment evolving over time, informative censoring is already introduced in unadjusted Kaplan-Meier curves. This leads to misrepresented survival chances in observed treatment groups. The resulting biased treatment association may be aggravated upon implementing IPW for treatment. Aware of additional challenges, we further recall how similar studies to date have selected patients into treatment groups based on events occurring post treatment initiation. Our study reveals the dramatic impact of resulting immortal time bias combined with other typical features of long-term incident disease registers, including missing covariates during the early phases of the register. We discuss feasible ways of accommodating these features when targeting relevant estimands, and demonstrate how more than one causal question can be answered relying on the no unmeasured baseline confounders assumption.
当从观察数据中得出因果推论时，失效时间结果通常会伴随着其他缺失数据模式的删失，这带来了额外的挑战。在本文中，我们以终末期肾病的发病病例为研究对象，考察了以移植（所谓的抢先性肾移植）作为起始治疗方案与以透析起始，可能随后延迟移植的方案相比，对全因死亡率的影响。问题相对简单：对于目标人群，哪种起始治疗方案预期会带来最佳生存？为了解决这个问题，我们模仿了一项基于长期瑞典肾脏登记的目标试验，该登记在全国范围内测量了越来越多的共同基线协变量。我们总结了一些更普遍适用于长期疾病登记的经验教训。随着病例特征和治疗版本随时间的演变，未调整的 Kaplan-Meier 曲线中已经引入了信息性删失。这导致观察治疗组中的生存机会被错误代表。在实施基于倾向评分的治疗时，这种偏倚的治疗关联可能会加剧。鉴于额外的挑战，我们进一步回忆起迄今为止，类似的研究如何根据治疗开始后发生的事件将患者选入治疗组。我们的研究揭示了由此产生的不朽时间偏倚与长期发病疾病登记的其他典型特征相结合的巨大影响，包括登记早期阶段缺失的协变量。我们讨论了在针对相关估计量时适应这些特征的可行方法，并展示了如何依靠无未测量的基线混杂因素假设回答多个因果问题。
