Ciocănea-Teodorescu Iuliana, Goetghebeur Els, Waernbaum Ingeborg, Schön Staffan, Gabriel Erin E
Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
Victor Babeş National Institute of Pathology, Bucharest, Romania.
Stat Med. 2023 May 30;42(12):1946-1964. doi: 10.1002/sim.9706. Epub 2023 Mar 8.
Long-term register data offer unique opportunities to explore causal effects of treatments on time-to-event outcomes, in well-characterized populations with minimum loss of follow-up. However, the structure of the data may pose methodological challenges. Motivated by the Swedish Renal Registry and estimation of survival differences for renal replacement therapies, we focus on the particular case when an important confounder is not recorded in the early period of the register, so that the entry date to the register deterministically predicts confounder missingness. In addition, an evolving composition of the treatment arms populations, and suspected improved survival outcomes in later periods lead to informative administrative censoring, unless the entry date is appropriately accounted for. We investigate different consequences of these issues on causal effect estimation following multiple imputation of the missing covariate data. We analyse the performance of different combinations of imputation models and estimation methods for the population average survival. We further evaluate the sensitivity of our results to the nature of censoring and misspecification of fitted models. We find that an imputation model including the cumulative baseline hazard, event indicator, covariates and interactions between the cumulative baseline hazard and covariates, followed by regression standardization, leads to the best estimation results overall, in simulations. Standardization has two advantages over inverse probability of treatment weighting here: it can directly account for the informative censoring by including the entry date as a covariate in the outcome model, and allows for straightforward variance computation using readily available software.
长期登记数据为探索治疗对事件发生时间结局的因果效应提供了独特机会，适用于特征明确且随访损失最小的人群。然而，数据结构可能带来方法学上的挑战。受瑞典肾脏登记处及肾脏替代疗法生存差异估计的启发，我们关注登记早期未记录重要混杂因素的特殊情况，使得登记的进入日期能确定性地预测混杂因素的缺失。此外，治疗组人群构成的变化以及后期疑似改善的生存结局会导致信息性行政删失，除非对进入日期进行适当考虑。我们研究了在对缺失协变量数据进行多次插补后，这些问题对因果效应估计的不同影响。我们分析了插补模型和总体平均生存估计方法的不同组合的性能。我们进一步评估了结果对删失性质和拟合模型误设的敏感性。我们发现，在模拟中，一个包含累积基线风险、事件指标、协变量以及累积基线风险与协变量之间相互作用的插补模型，随后进行回归标准化，总体上能带来最佳估计结果。在此，标准化相对于治疗权重的逆概率有两个优势：它可以通过在结局模型中纳入进入日期作为协变量直接考虑信息性删失，并且允许使用现成软件直接计算方差。
