Terrera Graciela Muniz, Matthews Fiona, Brayne Carol
MRC Biostatistics Unit, Institute of Public Health, Robinson Way, University Forvie Site, CB2 0SR, Cambridge, UK.
BMC Neurol. 2008 May 16;8:16. doi: 10.1186/1471-2377-8-16.
Cognitive decline is a major threat to well being in later life. Change scores and regression based models have often been used for its investigation. Most methods used to describe cognitive decline assume individuals lose their cognitive abilities at a constant rate with time. The investigation of the parametric curve that best describes the process has been prevented by restrictions imposed by study design limitations and methodological considerations. We propose a comparison of parametric shapes that could be considered to describe the process of cognitive decline in late life. Attrition plays a key role in the generation of missing observations in longitudinal studies of older persons. As ignoring missing observations will produce biased results and previous studies point to the important effect of the last observed cognitive score on the probability of dropout, we propose modelling both mechanisms jointly to account for these two considerations in the model likelihood.
Data from four interview waves of a population based longitudinal study of the older population, the Cambridge City over 75 Cohort Study were used. Within a selection model process, latent growth models combined with a logistic regression model for the missing data mechanism were fitted. To illustrate advantages of the model proposed, a sensitivity analysis of the missing data assumptions was conducted.
Results showed that a quadratic curve describes cognitive decline best. Significant heterogeneity between individuals about mean curve parameters was identified. At all interviews, MMSE scores before dropout were significantly lower than those who remained in the study. Individuals with good functional ability were found to be less likely to dropout, as were women and younger persons in later stages of the study.
The combination of a latent growth model with a model for the missing data has permitted to make use of all available data and quantify the effect of significant predictors of dropout on the dropout and observational processes. Cognitive decline over time in older persons is often modelled as a linear process, though we have presented other parametric curves that may be considered.
认知能力下降是晚年生活幸福的主要威胁。变化分数和基于回归的模型经常被用于对此进行研究。大多数用于描述认知能力下降的方法都假定个体的认知能力随时间以恒定速率丧失。由于研究设计限制和方法学考虑所施加的限制，对最能描述该过程的参数曲线的研究受到了阻碍。我们提议对可被视为描述晚年认知能力下降过程的参数形状进行比较。在对老年人的纵向研究中，失访在缺失观测值的产生中起着关键作用。由于忽略缺失观测值会产生有偏差的结果，且先前的研究指出最后一次观测到的认知分数对退出概率有重要影响，我们提议将这两种机制联合建模，以便在模型似然性中考虑这两个因素。
使用了基于人群的老年人纵向研究——剑桥市75岁以上队列研究的四轮访谈数据。在一个选择模型过程中，拟合了结合缺失数据机制的逻辑回归模型的潜在增长模型。为了说明所提议模型的优势，对缺失数据假设进行了敏感性分析。
结果表明二次曲线最能描述认知能力下降。识别出个体在平均曲线参数方面存在显著异质性。在所有访谈中，退出研究前的简易精神状态检查表（MMSE）分数显著低于留在研究中的人的分数。研究发现，功能能力良好的个体退出研究的可能性较小，在研究后期女性和年轻人也是如此。
潜在增长模型与缺失数据模型的结合使得能够利用所有可用数据，并量化退出的重要预测因素对退出和观测过程的影响。老年人随时间的认知能力下降通常被建模为一个线性过程，不过我们也提出了其他可能被考虑的参数曲线。
