Rehabilitation Medicine Department, National Institutes of Health, Bethesda, United States.
Rehabilitation Medicine Department, National Institutes of Health, Bethesda, United States.
J Biomech. 2024 Jan;162:111855. doi: 10.1016/j.jbiomech.2023.111855. Epub 2023 Oct 30.
In many aspects of human research, capturing multiple measures from the same participant is common due to the symmetric nature of the human body (e.g., two eyes, ten fingers, two legs, etc.). This has established a concerning paradox in biomedical and clinical research. When the same condition exist bilaterally (controls or bilateral pathology), researchers often blindly include both (or multiple) measures into the statistical analysis. This assumes that measures between the two sides are statistically independent (uncorrelated). However, there are certain inherent factors within an individual (e.g., age, sex, physical activity, gait pattern, tissue characteristics, hormonal status, pain thresholds, etc.) that would point to a statistical dependence between bilateral measures. Conversely, in unilateral pathology, it is common practice to use the contralateral side as the comparator. This assumes the exact opposite, that sans pathology, bilateral measures are perfectly correlated without bias. Both of these assumptions can lead to errors in the study conclusions. Few studies have explored the statistical dependence between multiple measures from the same participant. Thus, the purpose of this perspective is to explore the statistical considerations associated with analyzing multiple measures from the same participant and provide recommendations for navigating the use of multiple, non-temporal, data points from the same participant. To give context for these recommendations, an example dataset involving patellofemoral kinematics is provided. Due to the prevalent use of bilateral data in the current literature and the resulting potential for invalid study conclusions, we recommend that future research use caution when using multiple measures from the same participant and apply proper statistical analysis (e.g., generalized estimating equations) when these measures are not independent. If the contralateral limb is used as a comparator in unilateral pathology, strong evidence must exist that the underlying pathology has not altered the measures of interest in this contralateral limb.
在人类研究的许多方面,由于人体的对称性(例如,两只眼睛、十个手指、两条腿等),从同一参与者身上捕捉多个测量值是很常见的。这在生物医学和临床研究中建立了一个令人担忧的悖论。当相同的条件存在于双侧(对照或双侧病理)时,研究人员通常会盲目地将两者(或多个)测量值纳入统计分析。这假设两侧的测量值在统计学上是独立的(不相关的)。然而,个体内部存在某些内在因素(例如,年龄、性别、身体活动、步态模式、组织特征、激素状态、疼痛阈值等),这些因素表明双侧测量值之间存在统计学依赖。相反,在单侧病理中,常用对侧作为对照。这假设了完全相反的情况,即没有病理时,双侧测量值是完全相关的,没有偏差。这两个假设都可能导致研究结论出现错误。很少有研究探讨过来自同一参与者的多个测量值之间的统计依赖性。因此,本文的目的是探讨分析同一参与者的多个测量值所涉及的统计考虑因素,并为从同一参与者获得多个非时间相关数据点的使用提供建议。为了给这些建议提供背景,提供了一个涉及髌股关节运动学的示例数据集。由于当前文献中广泛使用双侧数据,以及由此产生的无效研究结论的潜在风险,我们建议未来的研究在使用同一参与者的多个测量值时要谨慎,并在这些测量值不独立时应用适当的统计分析(例如,广义估计方程)。如果在单侧病理中使用对侧肢体作为对照,则必须有强有力的证据表明,潜在的病理没有改变对侧肢体中感兴趣的测量值。