How balance and sample size impact bias in the estimation of causal treatment effects: A simulation study
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平衡性与样本量如何影响因果处理效应估计的偏差：一项模拟研究
- 研究问题
- 在观察性研究中，加权后的协变量平衡程度、样本量及模型设定如何共同影响因果处理效应的偏差与推断可靠性？
- 方法与思路
- 作者使用不同规模的模拟数据，考察处理分配、协变量与结果关系、加权平衡、混杂变量选择及样本量五类因素。
- 材料陈述的贡献
- 摘要称最大KS统计量比平均标准化均值差更适合衡量平衡，建议阈值0.1及每个混杂变量每组60—80个观测。
- 阅读关注点
- 应核对模拟中的处理机制、结果模型、混杂结构与重叠程度，以及平衡阈值和样本量建议适用的设计范围。
- 阅读边界
- 这是arXiv预印本，同行评审与期刊接收未核实；摘要中的阈值和样本量建议不能直接视为普遍保证。
原文摘要
Observational studies are often used to understand relationships between exposures and outcomes. They do not, however, allow conclusions about causal relationships to be drawn unless statistical techniques are used to account for the imbalance of confounders across exposure groups. Propensity score and balance weighting (PSBW) are useful techniques that aim to reduce the imbalances between exposure groups by weighting the groups to look alike on the observed confounders. Despite the plethora of available methods to estimate PSBW, there is little guidance on what one defines as adequate balance, and unbiased and robust estimation of the causal treatment effect is not guaranteed unless several conditions hold. Accurate inference requires that 1. the treatment allocation mechanism is known, 2. the relationship between the baseline covariates and the outcome is known, 3. adequate balance of baseline covariates is achieved post-weighting, 4. a proper set of covariates to control for confounding bias is known, and 5. a large enough sample size is available. In this article, we use simulated data of various sizes to investigate the influence of these five factors on statistical inference. Our findings provide evidence that the maximum Kolmogorov- Smirnov statistic is the proper statistical measure to assess balance on the baseline covariates, in contrast to the mean standardised mean difference used in many applications, and 0.1 is a suitable threshold to consider as acceptable balance. Finally, we recommend that 60-80 observations, per confounder per treatment group, are required to obtain a reliable and unbiased estimation of the causal treatment effect.