In observational studies, accurately characterizing variance is critical for sample size determination, yet unaccounted-for variability from propensity score estimation and the resulting weights limit the accuracy of standard variance approximations for design. Existing approaches often rely on heuristics or randomized controlled trial (RCT) formulas that treat weights as fixed, potentially misaligning prospective design with the causal estimator used at analysis. We propose an estimator-aligned framework for prospective sample size determination based on generalized estimating equations (GEE) and stacked M-estimation. By merging the propensity score model and marginal structural model (MSM) into a single system of estimating equations, the method propagates nuisance-model uncertainty and directly targets the large-sample variance of the IPTW estimator. For study planning, we estimate a pilot-based large-sample variance factor and introduce a bootstrap stabilization procedure that accounts for both within- and between-pilot variability. The framework applies uniformly across binary, count, and continuous outcomes through link-specific GEE representations under a common design principle. Simulation studies motivated by post-marketing safety and healthcare cost applications demonstrate that anchoring design to this variance improves power calibration relative to conventional RCT-style formulas, particularly in settings with weight instability, outcome sparsity, or heavy-tailed variability.

翻译：在观察性研究中，准确保存变异特征对于样本量确定至关重要，然而倾向性评分估计及其所得权重中未纳入的变异性限制了设计中标准方差近似的准确性。现有方法常依赖经验法则或随机对照试验（RCT）公式（将权重视为固定值），这可能导致前瞻性设计与分析阶段使用的因果估计量之间脱节。本文提出一种基于广义估计方程（GEE）与堆叠M估计的、与估计量对齐的前瞻性样本量确定框架。通过将倾向性评分模型与边际结构模型（MSM）融合为单一估计方程系统，该方法可传播干扰模型的不确定性，并直接针对逆概率治疗加权（IPTW）估计量的大样本方差。在研究规划阶段，我们基于预试验估计大样本方差因子，并引入一种考虑预试验内部及间变异性的自助法稳定化流程。该框架通过通用设计原则下基于连接函数的GEE表示，统一适用于二分类、计数及连续型结局。以上市后安全性和医疗成本应用为背景的模拟研究表明：与常规RCT风格公式相比，锚定该方差的优化设计可改善统计效力的校准，尤其在权重不稳定、结局稀疏或重尾变异的情景中效果显著。