Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference
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基于实质模型相容多重插补的结果缺失敏感性分析及因果应用
- 研究问题
- 当结果变量影响自身缺失概率时，如何开展非随机缺失敏感性分析，并保持插补模型与含交互项的因果分析模型相容？
- 方法与思路
- 作者将结果变量的 delta 调整分别加入 SMCFCS 和堆叠插补方案，形成两种相容敏感性分析方法，并开展模拟与案例应用。
- 材料陈述的贡献
- 摘要称，在所设模拟中，朴素 NARFCS 导致效应估计偏差，而两种相容扩展近似无偏，为因果分析的缺失敏感性评估提供方案。
- 阅读关注点
- 应核对 delta 参数的含义与取值依据、交互项如何进入插补模型，以及两种方案的不确定性合并和模拟缺失机制。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未说明敏感性参数范围或完整模型条件，近似无偏仅为所述模拟结果。
原文摘要
When using multiple imputation (MI) for missing data, maintaining compatibility between the imputation model and substantive analysis is important for avoiding bias. For example, some causal inference methods incorporate an outcome model with exposure-confounder interactions that must be reflected in the imputation model. Two approaches for compatible imputation with multivariable missingness have been proposed: Substantive-Model-Compatible Fully Conditional Specification (SMCFCS) and a stacked-imputation-based approach (SMC-stack). If the imputation model is correctly specified, both approaches are guaranteed to be unbiased under the "missing at random" assumption. However, this assumption is violated when the outcome causes its own missingness, which is common in practice. In such settings, sensitivity analyses are needed to assess the impact of alternative assumptions on results. An appealing solution for sensitivity analysis is delta-adjustment using MI, specifically "not-at-random" (NAR)FCS. However, the issue of imputation model compatibility has not been considered in sensitivity analysis, with a naive implementation of NARFCS being susceptible to bias. To address this gap, we propose two approaches for compatible sensitivity analysis when the outcome causes its own missingness. The proposed approaches, NAR-SMCFCS and NAR-SMC-stack, extend SMCFCS and SMC-stack, respectively, with delta-adjustment for the outcome. We evaluate these approaches using a simulation study that is motivated by a case study, to which the methods were also applied. The simulation results confirmed that a naive implementation of NARFCS produced bias in effect estimates, while NAR-SMCFCS and NAR-SMC-stack were approximately unbiased. The proposed compatible approaches provide promising avenues for conducting sensitivity analysis to missingness assumptions in causal inference.