The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-Data Applications
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以因果路线图和模拟提高真实数据应用的严谨性与可复现性
- 研究问题
- 真实数据应用中，如何在估计处理效应之前，借助贴近应用的模拟选择并预先规定估计与推断算法？
- 方法与思路
- 作者按因果路线图设计模拟，在数据收集后、效应估计前开展结果盲或处理盲比较，以确定TMLE分析方案。
- 材料陈述的贡献
- 摘要通过纵向观察研究和结局缺失的整群随机试验，说明模拟可辅助预设分析方案，并生成经过质量检查的分析代码。
- 阅读关注点
- 应核对模拟如何反映混杂、依赖和缺失，盲法如何落实，以及候选算法、选择标准和最终分析计划如何固定。
- 阅读边界
- 这是arXiv预印本，同行评审及接收状态未核实；摘要未给出模拟配置和误差控制数值，预期有限样本表现仍需核验。
原文摘要
The Causal Roadmap outlines a systematic approach to asking and answering questions of cause-and-effect: define the quantity of interest, evaluate needed assumptions, conduct statistical estimation, and carefully interpret results. To protect research integrity, it is essential that the algorithm for statistical estimation and inference be pre-specified prior to conducting any effectiveness analyses. However, it is often unclear which algorithm will perform optimally for the real-data application. Instead, there is a temptation to simply implement one's favorite algorithm -- recycling prior code or relying on the default settings of a computing package. Here, we call for the use of simulations that realistically reflect the application, including key characteristics such as strong confounding and dependent or missing outcomes, to objectively compare candidate estimators and facilitate full specification of the Statistical Analysis Plan. Such simulations are informed by the Causal Roadmap and conducted after data collection but prior to effect estimation. We illustrate with two worked examples. First, in an observational longitudinal study, outcome-blind simulations are used to inform nuisance parameter estimation and variance estimation for longitudinal targeted minimum loss-based estimation (TMLE). Second, in a cluster randomized trial with missing outcomes, treatment-blind simulations are used to examine Type-I error control in Two-Stage TMLE. In both examples, realistic simulations empower us to pre-specify an estimation approach that is expected to have strong finite sample performance and also yield quality-controlled computing code for the actual analysis. Together, this process helps to improve the rigor and reproducibility of our research.