Repeated Measures Designs and Analysis of Longitudinal Data: If at First You Do Not Succeed-Try, Try Again.
Repeated Measures Designs and Analysis of Longitudinal Data: If at First You Do Not Succeed-Try, Try Again.
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DOI：
10.1213/ane.0000000000003511
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发表时间：
2018-08
影响因子：
5.7
通讯作者：
Vetter TR
中科院分区：
文献类型：
--
作者：
Schober P;Vetter TR
麻醉、危重护理、围手术期和疼痛研究通常涉及研究设计，在相同的患者身上重复测量或观察相同的结果变量。这种重复测量的数据被称为纵向数据，纵向研究设计通常用于调查结果随时间的变化，并在治疗组之间比较这些变化。从统计学的角度来看，纵向研究通常会提高估计治疗效果的精确度，从而增加检测此类效果的能力。常用的统计技术大多假定观测或测量是独立的。然而，在同一个体中重复测量的值通常比不同个体的值彼此更相似，忽略重复测量之间的相关性可能导致估计偏差以及无效的P值和可信区间。因此，对重复测量数据的适当分析需要特定的统计技术。本教程回顾了3类常用的纵向数据分析方法。第一类使用汇总统计，将重复测量的信息浓缩为每个受试者的单个数字，从而基本上消除了受试者内部的重复测量，并允许使用标准统计假设检验对组进行直接比较。第二类在历史上很流行，包括方差类型的重复测量分析。然而，在实践中很少满足的强假设和低灵活性限制了这种方法的有效性。第三类包括现代和灵活的基于回归的技术，可以推广以适应广泛的结果数据，包括连续数据、分类数据和计数数据。这种方法可进一步分为所谓的“总体平均统计模型”和“特定对象模型”，前者侧重于通过广义估计方程估计结果的平均反应，后者通过使用随机效应来捕捉受试者内部的相关性，从而允许完全说明结果的分布。选择哪种方法在一定程度上取决于研究的目的和对估计效果的预期解释(总体平均解释与特定对象的解释)。本教程讨论了每种技术的理论背景，并结合发表在《麻醉与止痛学》上的具体研究实例，演示了这些技术是如何在实践中使用的。
Anesthesia, critical care, perioperative, and pain research often involves study designs in which the same outcome variable is repeatedly measured or observed over time on the same patients. Such repeatedly measured data are referred to as longitudinal data, and longitudinal study designs are commonly used to investigate changes in an outcome over time and to compare these changes among treatment groups. From a statistical perspective, longitudinal studies usually increase the precision of estimated treatment effects, thus increasing the power to detect such effects. Commonly used statistical techniques mostly assume independence of the observations or measurements. However, values repeatedly measured in the same individual will usually be more similar to each other than values of different individuals and ignoring the correlation between repeated measurements may lead to biased estimates as well as invalid P values and confidence intervals. Therefore, appropriate analysis of repeated-measures data requires specific statistical techniques. This tutorial reviews 3 classes of commonly used approaches for the analysis of longitudinal data. The first class uses summary statistics to condense the repeatedly measured information to a single number per subject, thus basically eliminating within-subject repeated measurements and allowing for a straightforward comparison of groups using standard statistical hypothesis tests. The second class is historically popular and comprises the repeated-measures analysis of variance type of analyses. However, strong assumptions that are seldom met in practice and low flexibility limit the usefulness of this approach. The third class comprises modern and flexible regression-based techniques that can be generalized to accommodate a wide range of outcome data including continuous, categorical, and count data. Such methods can be further divided into so-called “population-average statistical models” that focus on the specification of the mean response of the outcome estimated by generalized estimating equations, and “subject-specific models” that allow a full specification of the distribution of the outcome by using random effects to capture within-subject correlations. The choice as to which approach to choose partly depends on the aim of the research and the desired interpretation of the estimated effects (population-average versus subject-specific interpretation). This tutorial discusses aspects of the theoretical background for each technique, and with specific examples of studies published in Anesthesia & Analgesia, demonstrates how these techniques are used in practice.