A Comparison of Different Methods to Adjust Survival Curves for Confounders
调整生存曲线混杂因素的不同方法比较AI 解读 ↓
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调整生存曲线混杂因素的不同方法比较
- 研究问题
- 在非随机时间至事件研究中，不同混杂调整方法对整条处理组生存曲线的偏差、拟合表现和效率有何差异？
- 方法与思路
- 作者比较加权、G 公式、倾向得分匹配、经验似然及增广方法和伪值版本，结合吸烟应用与不同模型设定的模拟。
- 材料陈述的贡献
- 摘要称，所考察情景中多数方法在中大样本无系统偏差；部分 Cox 方法有小样本偏差，双重稳健方法表现出若干优势。
- 阅读关注点
- 应核对删失处理、混杂调整条件、曲线整体评价指标、模型误设方式，以及双重稳健结论覆盖哪些生存模型与估计目标。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审或期刊接收未核实；摘要未列模拟参数与数值，其优势陈述仅覆盖所考察情景。
原文摘要
Treatment specific survival curves are an important tool to illustrate the treatment effect in studies with time-to-event outcomes. In non-randomized studies, unadjusted estimates can lead to biased depictions due to confounding. Multiple methods to adjust survival curves for confounders exist. However, it is currently unclear which method is the most appropriate in which situation. Our goal is to compare forms of Inverse Probability of Treatment Weighting, the G-Formula, Propensity Score Matching, Empirical Likelihood Estimation and augmented estimators as well as their pseudo-values based counterparts in different scenarios with a focus on their bias and goodness-of-fit. We provide a short review of all methods and illustrate their usage by contrasting the survival of smokers and non-smokers, using data from the German Epidemiological Trial on Ankle-Brachial-Index. Subsequently, we compare the methods using a Monte-Carlo simulation. We consider scenarios in which correctly or incorrectly specified models for describing the treatment assignment and the time-to-event outcome are used with varying sample sizes. The bias and goodness-of-fit is determined by taking the entire survival curve into account. When used properly, all methods showed no systematic bias in medium to large samples. Cox regression based methods, however, showed systematic bias in small samples. The goodness-of-fit varied greatly between different methods and scenarios. Methods utilizing an outcome model were more efficient than other techniques, while augmented estimators using an additional treatment assignment model were unbiased when either model was correct with a goodness-of-fit comparable to other methods. These doubly-robust methods have important advantages in every considered scenario.