Background: Pooled logistic regression models are commonly applied in survival analysis. However, the standard implementation can be computationally demanding, which is further exacerbated when using the nonparametric bootstrap for inference. To ease these computational burdens, investigators often coarsen time intervals or assume a parametric models for time. These approaches impose restrictive assumptions, which may not always have a well-motivated substantive justification. Methods: Here, the pooled logistic regression model is re-framed using estimating equations to simplify computations and allow for inference via the empirical sandwich variance estimator, thus avoiding the more computationally demanding bootstrap. The proposed implementation is demonstrated using two examples with publicly available data. The performance of the empirical sandwich variance estimator is illustrated using a Monte Carlo simulation study. Results: As shown in the applied examples, the proposed implementation substantially reduced run-times and could be applied without needing to coarsen the data. In the simulation study, the empirical sandwich variance estimator results in nominal confidence interval coverage. Conclusions: The implementation proposed here offers an improved alternative to the standard implementation of pooled logistic regression without needing to impose restrictive constraints on time.

翻译：背景：汇集逻辑回归模型常用于生存分析，但标准实现的计算负担较大，使用非参数自助法进行推断时这一问题进一步加剧。为缓解计算压力，研究者常采用时间间隔粗化或假设时间参数模型等方法，但这些方法施加了严格假设，可能缺乏充分的实质性依据。方法：本文通过估计方程重新构建汇集逻辑回归模型，简化计算并利用经验三明治方差估计器实现推断，从而避免了计算更繁重的自助法。通过两个公开数据示例验证所提实现方案，并采用蒙特卡洛模拟研究评估经验三明治方差估计器的性能。结果：应用示例表明，所提实现方案显著缩短运行时间，且无需对数据进行粗化处理。模拟研究中，经验三明治方差估计器实现了标称置信区间覆盖率。结论：本文提出的实现方案为标准汇集逻辑回归提供了一种改进替代方法，无需对时间施加严格约束限制。