MOVER confidence intervals for a difference or ratio effect parameter under stratified sampling
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分层抽样下差值或比值效应参数的 MOVER 置信区间
- 研究问题
- 分层抽样中，如何利用各层各组已有的点估计、置信区间与方差信息，为总体差值或比值效应构造便于实施的置信区间？
- 方法与思路
- 作者以方差估计恢复法构造差值的加性方差与加性区间方案，并用 Fieller 和对数比值法构造比值区间，区分固定与随机权重。
- 材料陈述的贡献
- 摘要称，随机权重方案要求层间效应恒定，固定权重无需该要求；模拟中所提区间总体优于标准大样本区间，并给出两类实例。
- 阅读关注点
- 应核对权重对应的目标参数、层间恒定效应要求、比值分母接近零时的区间行为，以及生存结局中各层输入量的构造。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未报告模拟设计与覆盖率细节，所述总体优势不构成普遍性能保证。
原文摘要
Stratification is commonly employed in clinical trials to reduce the chance covariate imbalances and increase the precision of the treatment effect estimate. We propose a general framework for constructing the confidence interval (CI) for a difference or ratio effect parameter under stratified sampling by the method of variance estimates recovery (MOVER). We consider the additive variance and additive CI approaches for the difference, in which either the CI for the weighted difference, or the CI for the weighted effect in each group, or the variance for the weighted difference is calculated as the weighted sum of the corresponding stratum-specific statistics. The CI for the ratio is derived by the Fieller and log-ratio methods. The weights can be random quantities under the assumption of a constant effect across strata, but this assumption is not needed for fixed weights. These methods can be easily applied to different endpoints in that they require only the point estimate, CI, and variance estimate for the measure of interest in each group across strata. The methods are illustrated with two real examples. In one example, we derive the MOVER CIs for the risk difference and risk ratio for binary outcomes. In the other example, we compare the restricted mean survival time and milestone survival in stratified analysis of time-to-event outcomes. Simulations show that the proposed MOVER CIs generally outperform the standard large sample CIs, and that the additive CI approach performs slightly better than the additive variance approach.