Functional Principal Component Analysis as an Alternative to Mixed-Effect Models for Describing Sparse Repeated Measures in Presence of Missing Data.

Analyzing longitudinal data in health studies is challenging due to sparse and error-prone measurements, strong within-individual correlation, missing data and various trajectory shapes. While mixed-effect models (MM) effectively address these challenges, they remain parametric models and may incur computational costs. In contrast, functional principal component analysis (FPCA) is a non-parametric approach developed for regular and dense functional data that flexibly describes temporal trajectories at a potentially lower computational cost. This article presents an empirical simulation study evaluating the behavior of FPCA with sparse and error-prone repeated measures and its robustness under different missing data schemes in comparison with MM. The results show that FPCA is well-suited in the presence of missing at random data caused by dropout, except in scenarios involving most frequent and systematic dropout. Like MM, FPCA fails under missing not at random mechanism. The FPCA was applied to describe the trajectories of four cognitive functions before clinical dementia and contrast them with those of matched controls in a case-control study nested in a population-based aging cohort. The average cognitive declines of future dementia cases showed a sudden divergence from those of their matched controls with a sharp acceleration 5 to 2.5 years prior to diagnosis.

Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach.

BACKGROUND: The individual data collected throughout patient follow-up constitute crucial information for assessing the risk of a clinical event, and eventually for adapting a therapeutic strategy. Joint models and landmark models have been proposed to compute individual dynamic predictions from repeated measures to one or two markers. However, they hardly extend to the case where the patient history includes much more repeated markers. Our objective was thus to propose a solution for the dynamic prediction of a health event that may exploit repeated measures of a possibly large number of markers. METHODS: We combined a landmark approach extended to endogenous markers history with machine learning methods adapted to survival data. Each marker trajectory is modeled using the information collected up to the landmark time, and summary variables that best capture the individual trajectories are derived. These summaries and additional covariates are then included in different prediction methods adapted to survival data, namely regularized regressions and random survival forests, to predict the event from the landmark time. We also show how predictive tools can be combined into a superlearner. The performances are evaluated by cross-validation using estimators of Brier Score and the area under the Receiver Operating Characteristic curve adapted to censored data. RESULTS: We demonstrate in a simulation study the benefits of machine learning survival methods over standard survival models, especially in the case of numerous and/or nonlinear relationships between the predictors and the event. We then applied the methodology in two prediction contexts: a clinical context with the prediction of death in primary biliary cholangitis, and a public health context with age-specific prediction of death in the general elderly population. CONCLUSIONS: Our methodology, implemented in R, enables the prediction of an event using the entire longitudinal patient history, even when the number of repeated markers is large. Although introduced with mixed models for the repeated markers and methods for a single right censored time-to-event, the technique can be used with any other appropriate modeling technique for the markers and can be easily extended to competing risks setting.

Predicting hemispheric dominance for language production in healthy individuals using support vector machine.

We used a Support Vector Machine (SVM) classifier to assess hemispheric pattern of language dominance of 47 individuals categorized as non-typical for language from their hemispheric functional laterality index (HFLI) measured on a sentence minus word-list production fMRI-BOLD contrast map. The SVM classifier was trained at discriminating between Dominant and Non-Dominant hemispheric language production activation pattern on a group of 250 participants previously identified as Typicals (HFLI strongly leftward). Then, SVM was applied to each hemispheric language activation pattern of 47 non-typical individuals. The results showed that at least one hemisphere (left or right) was found to be Dominant in every, except 3 individuals, indicating that the "dominant" type of functional organization is the most frequent in non-typicals. Specifically, left hemisphere dominance was predicted in all non-typical right-handers (RH) and in 57.4% of non-typical left-handers (LH). When both hemisphere classifications were jointly considered, four types of brain patterns were observed. The most often predicted pattern (51%) was left-dominant (Dominant left-hemisphere and Non-Dominant right-hemisphere), followed by right-dominant (23%, Dominant right-hemisphere and Non-Dominant left-hemisphere) and co-dominant (19%, 2 Dominant hemispheres) patterns. Co-non-dominant was rare (6%, 2 Non-Dominant hemispheres), but was normal variants of hemispheric specialization. In RH, only left-dominant (72%) and co-dominant patterns were detected, while for LH, all types were found, although with different occurrences. Among the 10 LH with a strong rightward HFLI, 8 had a right-dominant brain pattern. Whole-brain analysis of the right-dominant pattern group confirmed that it exhibited a functional organization strictly mirroring that of left-dominant pattern group. Hum Brain Mapp 38:5871-5889, 2017. © 2017 Wiley Periodicals, Inc.

Random survival forests with multivariate longitudinal endogenous covariates.

Predicting the individual risk of clinical events using the complete patient history is a major challenge in personalized medicine. Analytical methods have to account for a possibly large number of time-dependent predictors, which are often characterized by irregular and error-prone measurements, and are truncated early by the event. In this work, we extended the competing-risk random survival forests to handle such endogenous longitudinal predictors when predicting event probabilities. The method, implemented in the R package DynForest, internally transforms the time-dependent predictors at each node of each tree into time-fixed features (using mixed models) that can then be used as splitting candidates. The final individual event probability is computed as the average of leaf-specific Aalen-Johansen estimators over the trees. Using simulations, we compared the performances of DynForest to accurately predict an event with (i) a joint modeling alternative when considering two longitudinal predictors only, and with (ii) a regression calibration method that ignores the informative truncation by the event when dealing with a large number of longitudinal predictors. Through an application in dementia research, we also illustrated how DynForest can be used to develop a dynamic prediction tool for dementia from multimodal repeated markers, and quantify the importance of each marker.

Random forests for high-dimensional longitudinal data.

Random forests are one of the state-of-the-art supervised machine learning methods and achieve good performance in high-dimensional settings where p, the number of predictors, is much larger than n, the number of observations. Repeated measurements provide, in general, additional information, hence they are worth accounted especially when analyzing high-dimensional data. Tree-based methods have already been adapted to clustered and longitudinal data by using a semi-parametric mixed effects model, in which the non-parametric part is estimated using regression trees or random forests. We propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. Through simulation experiments, we then study the behavior of different estimation methods, especially in the context of high-dimensional data. Finally, the proposed method has been applied to an HIV vaccine trial including 17 HIV-infected patients with 10 repeated measurements of 20,000 gene transcripts and blood concentration of human immunodeficiency virus RNA. The approach selected 21 gene transcripts for which the association with HIV viral load was fully relevant and consistent with results observed during primary infection.

Prevalence and Co-Occurrence of Geriatric Syndromes in People Aged 75 Years and Older in France: Results From the Bordeaux Three-city Study.

BACKGROUND: Geriatric syndromes (GSs) are often the result of cumulative insults to multiple organ systems and are considered common in older adults. However, their frequency and co-occurrence are not well known in the elderly population. This study aimed to determine the prevalence of several GSs and to analyze the co-occurrence of these syndromes in a general population of elderly individuals. METHODS: A cross-sectional analysis of 630 adults aged 75 years or older participating in the 10-year follow-up of the Bordeaux sample of the French Three-City Study was conducted. The following 10 GSs were assessed: physical frailty, dementia and cognitive impairment, depressive symptoms, polymedication, social isolation, thinness, falls, dependence, sensory deficit, and incontinence. The prevalence of the 10 GSs was estimated, and multiple correspondence analysis (MCA) models were used to explore the mutual associations between these GSs. RESULTS: The mean age of the participants was 83.3 years; 69% were women, and 80.5% [95% confidence interval (CI) = 76.3-82.7] had at least one GS. The most frequent GSs were polymedication (50.6% 95%CI = 46.7-54.5) and falls (43.1% 95%CI = 38.4-46.1). The MCA models identified two major dimensions of the 10 GSs: "Dementia-Dependence-Incontinence" and "Frailty-Depression-Isolation." CONCLUSIONS: GSs were very common in this French elderly population and were grouped into two major dimensions: the "Dementia-Dependence-Incontinence" and "Frailty-Depression-Isolation."