SelectionBias: An R Package for Bounding Selection Bias in Causal Estimands
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SelectionBias：计算因果目标量选择偏差界的 R 包
- 研究问题
- 退出、缺失或样本筛选造成选择偏差时，如何对总体及被选择人群的因果风险比和风险差进行界限敏感性分析？
- 方法与思路
- 作者实现五类既有选择偏差界，结合观测数据及不同敏感性参数，在模拟公共卫生案例中演示并比较 EValue。
- 材料陈述的贡献
- 摘要的贡献是将多种选择偏差界整合为 R 软件工具，展示不同界及软件的功能差异，而非宣称这些界均为新理论。
- 阅读关注点
- 应核对各界对应的目标人群、输入参数及锐界条件，以及所谓无假设界的含义和两个软件比较的适用场景。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未给出参数设定规则，模拟寨卡案例不能视为真实疫情证据。
原文摘要
Selection bias may arise when there are dropouts or missing data in the analysis, or when subjects are included or excluded in the analysis based upon some selection criteria for the study population. Selection bias can jeopardize the validity of the study and a sensitivity analysis for assessing the effect of the selection is desired. Recently, there has been a surge of results for selection bias in causal inference, with several suggestions for sensitivity analyses. One method is to construct bounds for the bias. Here, we present the R package SelectionBias that can be used to calculate previously proposed bounds for selection bias for the causal risk ratio and causal risk difference for both the total and the selected populations. The first bound, derived by Smith and VanderWeele (SV), is based on values of sensitivity parameters that describe parts of the joint distribution of the outcome, treatment, selection indicator and unobserved variables. The second bound is an improved sharp bound that uses the same sensitivity parameters as the SV bound. The third bound is based solely on the observed data, and is therefore referred to as the assumption-free (AF) bound. The fourth and fifth bounds, the generalized assumption-free (GAF) and counterfactual assumption-free (CAF) bounds, utilize both the data and sensitivity parameters. The R package is illustrated with a simulated dataset that emulates a study where the effect of the zika virus on microcephaly in Brazil is investigated. Lastly, its performance and features are compared to the already existing R package EValue, highlighting situations where the two packages provide distinct advantages over each other.