Hengelbrock Johannes, Konietschke Frank, Herm Juliane, Audebert Heinrich, Aigner Annette
Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin, Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Department of Neurology and Center for Stroke Research Berlin, Freie Universität Berlin and Humboldt-Universität Zu Berlin, Campus Benjamin Franklin, Charité - Universitätsmedizin, Berlin, Germany.
BMC Med Res Methodol. 2025 Feb 27;25(1):53. doi: 10.1186/s12874-025-02497-2.
Clinical studies often aim to test the non-inferiority of a treatment compared to an alternative intervention with binary matched-pairs data. These studies are often planned with methods for completely observed pairs only. However, if missingness is more frequent than expected or is anticipated in the planning phase, methods are needed that allow the inclusion of partially observed pairs to improve statistical power.
We propose a flexible generalized estimating equations (GEE) approach to estimate confidence intervals for the risk difference, which accommodates partially observed pairs. Using simulated data, we compare this approach to alternative methods for completely observed pairs only and to those that also include pairs with missing observations. Additionally, we reconsider the study sample size calculation by applying these methods to a study with binary matched-pairs setting.
In moderate to large sample sizes, the proposed GEE approach performs similarly to alternative methods for completely observed pairs only. It even results in a higher power and narrower interval widths in scenarios with missing data and where missingness follows a missing (completely) at random (MCAR / MAR) mechanism. The GEE approach is also non-inferior to alternative methods, such as multiple imputation or confidence intervals explicitly developed for missing data settings. Reconsidering the sample size calculation for an observational study, our proposed approach leads to a considerably smaller sample size than the alternative methods.
Our results indicate that the proposed GEE approach is a powerful alternative to existing methods and can be used for testing non-inferiority, even if the initial sample size calculation was based on a different statistical method. Furthermore, it increases the analytical flexibility by allowing the inclusion of additional covariates, in contrast to other methods.
临床研究通常旨在通过二元配对数据检验一种治疗方法相对于另一种替代干预措施的非劣效性。这些研究通常仅采用针对完全观察到的配对的方法进行设计。然而，如果缺失情况比预期更频繁或在规划阶段就已预计到，那么就需要能够纳入部分观察到的配对以提高统计功效的方法。
我们提出一种灵活的广义估计方程（GEE）方法来估计风险差异的置信区间，该方法适用于部分观察到的配对。使用模拟数据，我们将这种方法与仅针对完全观察到的配对的替代方法以及那些也纳入有缺失观察值的配对的方法进行比较。此外，我们通过将这些方法应用于一个二元配对设置的研究来重新考虑研究样本量的计算。
在中等至大样本量的情况下，所提出的GEE方法的表现与仅针对完全观察到的配对的替代方法相似。在存在缺失数据且缺失情况遵循随机缺失（完全随机缺失/随机缺失，MCAR/MAR）机制的情况下，它甚至具有更高的功效和更窄的区间宽度。GEE方法也不劣于替代方法，如多重插补或专门为缺失数据设置开发的置信区间。重新考虑一项观察性研究的样本量计算，我们提出的方法导致的样本量比替代方法小得多。
我们的结果表明，所提出的GEE方法是现有方法的有力替代方法，即使初始样本量计算基于不同的统计方法，也可用于检验非劣效性。此外，与其他方法相比，它通过允许纳入额外的协变量增加了分析的灵活性。
