在临床和研究吞咽评估期间进行多次推注试验，以全面了解个体的吞咽功能。尽管从这些推注中获得了有价值的信息，但使用单次推注（例如最差分数）来描述功能障碍的程度仍然是常见的做法。研究人员还经常将连续或顺序吞咽测量数据分类，这可能会加剧信息丢失。这些做法可能会对检测和估计较小但可能有意义的治疗效果的统计能力产生不利影响。本研究旨在探讨渗透-渴望量表 (PAS) 分数的汇总和分类对统计功效和效应大小估计的影响。我们使用蒙特卡罗方法模拟了帕金森病和头颈癌的三项假设的受试者内治疗研究，涉及一系列数据特征（例如样本量、推注试验数量、变异性）。采用不同的统计模型（聚合或多级）以及各种 PAS 减少方法（即分类类型）来检查它们对效果大小估计的功效和准确性的影响。在所有场景中，与聚合（最差分数）模型相比，多级模型在检测群体级纵向变化方面表现出更高的统计能力和更准确的估计。与顺序方法相比，对 PAS 分数进行分类还降低了功效和偏差效应大小估计，尽管这取决于分类类型和基线 PAS 分布。多级模型应被视为一种更稳健的方法，用于对标准化吞咽方案中多次推注进行统计分析，因为它具有较高的敏感性和准确性，可以比较吞咽功能的组级变化。重要的是，这一发现在具有不同病理生理学（即 PD 和 HNC）和气道侵袭模式的患者群体中似乎是一致的。对连续结果或顺序结果进行分类的决定应基于临床或研究问题，并认识到规模缩小可能会对某些情况下的统计推断质量产生负面影响。© 2024。作者，获得 Springer Science 独家许可Business Media, LLC，隶属于施普林格自然。

Multiple bolus trials are administered during clinical and research swallowing assessments to comprehensively capture an individual's swallowing function. Despite valuable information obtained from these boluses, it remains common practice to use a single bolus (e.g., the worst score) to describe the degree of dysfunction. Researchers also often collapse continuous or ordinal swallowing measures into categories, potentially exacerbating information loss. These practices may adversely affect statistical power to detect and estimate smaller, yet potentially meaningful, treatment effects. This study sought to examine the impact of aggregating and categorizing penetration-aspiration scale (PAS) scores on statistical power and effect size estimates. We used a Monte Carlo approach to simulate three hypothetical within-subject treatment studies in Parkinson's disease and head and neck cancer across a range of data characteristics (e.g., sample size, number of bolus trials, variability). Different statistical models (aggregated or multilevel) as well as various PAS reduction approaches (i.e., types of categorizations) were performed to examine their impact on power and the accuracy of effect size estimates. Across all scenarios, multilevel models demonstrated higher statistical power to detect group-level longitudinal change and more accurate estimates compared to aggregated (worst score) models. Categorizing PAS scores also reduced power and biased effect size estimates compared to an ordinal approach, though this depended on the type of categorization and baseline PAS distribution. Multilevel models should be considered as a more robust approach for the statistical analysis of multiple boluses administered in standardized swallowing protocols due to its high sensitivity and accuracy to compare group-level changes in swallowing function. Importantly, this finding appears to be consistent across patient populations with distinct pathophysiology (i.e., PD and HNC) and patterns of airway invasion. The decision to categorize a continuous or ordinal outcome should be grounded in the clinical or research question with recognition that scale reduction may negatively affect the quality of statistical inferences in certain scenarios.© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.