Reweighting the RCT for generalization: finite sample error and variable selection
随机试验重加权推广：有限样本误差与变量选择AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究随机试验处理效应向目标总体的推广、重加权及相关变量选择。
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随机试验重加权推广：有限样本误差与变量选择
- 研究问题
- 当随机试验样本不代表目标总体时，抽样倾向逆概率加权的偏差、方差和风险如何受两个样本量及协变量选择影响？
- 方法与思路
- 作者在分类协变量设置下推导各样本量对应的精确偏差与方差，比较不同加权估计量，并分析额外协变量对渐近方差的影响。
- 材料陈述的贡献
- 摘要称，研究给出一致性证明和有限样本风险比较，并揭示估计处理分配概率及不同类型协变量纳入对估计表现的影响。
- 阅读关注点
- 应核对试验效应向目标总体推广的识别条件、空类别等有限样本问题，以及协变量分布迁移与效应修饰如何影响方差。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及接收状态未核实；精确误差结果针对分类协变量，摘要未列完整条件，不能直接推广至连续情形。
原文摘要
Randomized Controlled Trials (RCTs) may suffer from limited scope. In particular, samples may be unrepresentative: some RCTs over- or under- sample individuals with certain characteristics compared to the target population, for which one wants conclusions on treatment effectiveness. Re-weighting trial individuals to match the target population can improve the treatment effect estimation. In this work, we establish the exact expressions of the bias and variance of such reweighting procedures -- also called Inverse Propensity of Sampling Weighting (IPSW) -- in presence of categorical covariates for any sample size. Such results allow us to compare the theoretical performance of different versions of IPSW estimates. Besides, our results show how the performance (bias, variance, and quadratic risk) of IPSW estimates depends on the two sample sizes (RCT and target population). A by-product of our work is the proof of consistency of IPSW estimates. Results also reveal that IPSW performances are improved when the trial probability to be treated is estimated (rather than using its oracle counterpart). In addition, we study choice of variables: how including covariates that are not necessary for identifiability of the causal effect may impact the asymptotic variance. Including covariates that are shifted between the two samples but not treatment effect modifiers increases the variance while non-shifted but treatment effect modifiers do not. We illustrate all the takeaways in a didactic example, and on a semi-synthetic simulation inspired from critical care medicine.