Department of Biostatistics, Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA.
Alzheimer's Disease Research Center, Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
J Alzheimers Dis. 2022;87(1):489-501. doi: 10.3233/JAD-215553.
Accurate longitudinal modelling of cognitive decline is a major goal of Alzheimer's disease and related dementia (ADRD) research. However, the impact of subject-specific effects is not well characterized and may have implications for data generation and prediction.
This study seeks to address the impact of subject-specific effects, which are a less well-characterized aspect of ADRD cognitive decline, as measured by the Alzheimer's Disease Assessment Scale's Cognitive Subscale (ADAS-Cog).
Prediction errors and biases for the ADAS-Cog subscale were evaluated when using only population-level effects, robust imputation of subject-specific effects using model covariances, and directly known individual-level effects fit during modelling as a natural control. Evaluated models included pre-specified parameterizations for clinical trial simulation, analogous mixed-effects regression models parameterized directly, and random forest ensemble models. Assessment used a meta-database of Alzheimer's disease studies with validation in simulated synthetic cohorts.
All models observed increases in variance under imputation leading to increased prediction error. Bias decreased with imputation except under the pre-specified parameterization, which increased in the meta-database, but was attenuated under simulation. Known fitted subject effects gave the best prediction results.
Subject-specific effects were found to have a profound impact on predicting ADAS-Cog. Reductions in bias suggest imputing random effects assists in calculating results on average, as when simulating clinical trials. However, reduction in error emphasizes population-level effects when attempting to predict outcomes for individuals. Forecasting future observations greatly benefits from using known subject-specific effects.
准确的认知衰退纵向建模是阿尔茨海默病和相关痴呆症（ADRD）研究的主要目标。然而，个体效应的影响尚未得到很好的描述，这可能会对数据生成和预测产生影响。
本研究旨在探讨个体效应的影响，这是 ADRD 认知衰退中一个特征不太明显的方面，使用阿尔茨海默病评估量表认知子量表（ADAS-Cog）进行测量。
当仅使用群体水平效应、使用模型协方差稳健推断个体水平效应以及在建模过程中直接拟合已知的个体水平效应作为自然对照时，评估了 ADAS-Cog 子量表的预测误差和偏差。评估的模型包括临床试验模拟的预指定参数化、直接参数化的类似混合效应回归模型以及随机森林集成模型。评估使用了一个阿尔茨海默病研究的元数据库，并在模拟合成队列中进行了验证。
所有模型在推断时都观察到方差增加，导致预测误差增加。除了预指定参数化之外，偏差随着推断而减小，在元数据库中增加，但在模拟中减弱。已知拟合的个体效应给出了最佳的预测结果。
发现个体效应对预测 ADAS-Cog 具有深远影响。偏差的减少表明推断随机效应有助于平均计算结果，例如在模拟临床试验时。然而，误差的减少强调了在尝试预测个体结果时群体水平效应的重要性。预测未来的观察结果从使用已知的个体特定效应中大大受益。
