Department of Individual Differences and Psychological Assessment, University of Cologne, Pohligstraße 1, 50969, Cologne, Germany.
Department of Neurology, University Medicine Greifswald, Walther-Rathenau Str. 49, 17489, Greifswald, Germany.
BMC Med Res Methodol. 2020 Dec 7;20(1):296. doi: 10.1186/s12874-020-01176-8.
Even though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions.
We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) × 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability.
Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges.
Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain.
尽管在个体化医学的背景下，研究干预成功的预测因素（例如认知训练，CT）越来越受到关注，但由于使用了不同的且有时不适当的统计方法，因此总体领域中干预成功的具体预测因素的结果存在差异且不一致。因此，本文提供了关于适当使用多元回归分析来确定 CT 和类似非药物干预成功预测因素的指导。
我们基于预设的真实模型模拟数据，并进行了一系列不同的分析，以评估它们在检索真实模型系数方面的性能。真实模型由一个 2（组间：实验组与对照组）×2（组内：治疗前与治疗后）设计组成，有两个连续的预测因子，其中一个预测干预组的成功，另一个则没有。在分析数据时，我们考虑了四个常用的因变量（后测分数、绝对变化分数、相对变化分数、残差分数）、五个回归模型、八个样本量和四个可靠性水平。
我们的结果表明，一个回归模型，其中包括研究预测因子、组（实验组与对照组）、治疗前分数以及研究预测因子与组之间的交互作用作为预测因子，以绝对变化分数作为因变量，对于给定的实验设计最为方便。虽然出于统计功效的原因，应该将治疗前分数作为预测因子纳入回归模型，但不应该解释其系数，因为即使没有真正的关系，通常也会出现负的且具有统计学意义的回归系数。
通过模拟方法、理论推理和数学推导，我们能够针对 CT 研究和 CT 成功的外部预测因素中最常见的实验设计之一的数据分析提出建议。这些见解可以促进在未来研究中应用考虑到的数据分析，并促进累积知识的获取。
