An increasing number of large-scale multi-modal research initiatives has been conducted in the typically developing population, as well as in psychiatric cohorts. Missing data is a common problem in such datasets due to the difficulty of assessing multiple measures on a large number of participants. The consequences of missing data accumulate when researchers aim to explore relationships between multiple measures. Here we aim to evaluate different imputation strategies to fill in missing values in clinical data from a large (total N=764) and deeply characterised (i.e. range of clinical and cognitive instruments administered) sample of N=453 autistic individuals and N=311 control individuals recruited as part of the EU-AIMS Longitudinal European Autism Project (LEAP) consortium. In particular we consider a total of 160 clinical measures divided in 15 overlapping subsets of participants. We use two simple but common univariate strategies, mean and median imputation, as well as a Round Robin regression approach involving four independent multivariate regression models including a linear model, Bayesian Ridge regression, as well as several non-linear models, Decision Trees, Extra Trees and K-Neighbours regression. We evaluate the models using the traditional mean square error towards removed available data, and consider in addition the KL divergence between the observed and the imputed distributions. We show that all of the multivariate approaches tested provide a substantial improvement compared to typical univariate approaches. Further, our analyses reveal that across all 15 data-subsets tested, an Extra Trees regression approach provided the best global results. This allows the selection of a unique model to impute missing data for the LEAP project and deliver a fixed set of imputed clinical data to be used by researchers working with the LEAP dataset in the future.
翻译:在典型的发展中人口以及精神病组群中,开展了越来越多的大规模多模式研究举措。由于难以评估对众多参与者的多种措施,缺少的数据是这类数据集中常见的一个问题。当研究人员试图探索多种措施之间的关系时,缺少的数据积累的后果。我们的目标是评价不同的估算战略,以填补临床数据中缺失的数值,来自一个大(总计N=764)的临床数据,并具有深刻特征(即所管理的临床和认知仪器范围)的典型自闭症患者样本和N=311控制作为欧盟-AIMS长期欧洲自闭症项目(LEAP)联合体的一部分而征聘的个人。我们特别考虑到总共160项临床计量措施在15个重叠参与者子集中被分割。我们使用两种简单但常见的自闭状态战略,以及一轮罗宾回归方法,包括四种独立的多变回归模型,包括线性模型、巴耶斯海脊回归测试回归,以及一些非线性模型、决定树、外树和K-亲属自闭症(LEAP)的自闭症(L)控制个人。我们用的是总共160个临床诊断方法来评估了15个参与者相重叠分析。我们观察到的所有数据分布分析,我们用一个数据流数据流数据流数据分布分析,我们用一个已观察到的模型来显示的模型,我们观察到的模型分析。我们用一个显示的模型来显示的模型来显示的模型,用来显示所有平方差数据。