1.1k citations · 1.4k across the 20 of their papers we have counts for
22 papers
Neural ODE enhanced linear mixed effect models for estimating complex association patterns of time-varying covariates with the marker trajectory
Zhe Aurore Li, Quentin Clairon, Cécilia Samieri +3
Longitudinal cohort studies produce repeated data that enable the assessment of time-varying association patterns between exposures and health outcomes. Classical linear mixed-effe…
Patient-perceived progression in multiple system atrophy: natural history of quality of life
Tiphaine Saulnier, Margherita Fabbri, Mélanie Le Goff +6
Health-related quality of life (Hr-QoL) scales provide crucial information on neurodegenerative disease progression, help improving patient care, and constitute a meaningful endpoi…
Random Forests for time-fixed and time-dependent predictors: The DynForest R package
Anthony Devaux, Cécile Proust-Lima, Robin Genuer
The R package DynForest implements random forests for predicting a continuous, a categorical or a (multiple causes) time-to-event outcome based on time-fixed and time-dependent pre…
Disease progression model anchored around clinical diagnosis in longitudinal cohorts: example of Alzheimer's disease and related dementia
Jérémie Lespinasse, Carole Dufouil, Cécile Proust-Lima +1
Background. Alzheimer's disease and related dementia (ADRD) are characterized by multiple and progressive anatomo clinical changes. Yet, modeling changes over disease course from c…
Analysis of the 24-Hour Activity Cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought (ACT) Study
Yinxiang Wu, Dori E. Rosenberg, Mikael Anne Greenwood-Hickman +7
The 24-hour activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach captures the interrelatedness of the daily time s…
Random survival forests with multivariate longitudinal endogenous covariates
Anthony Devaux, Catherine Helmer, Robin Genuer +1
Predicting the individual risk of a clinical event using the complete patient history is still a major challenge for personalized medicine. Among the methods developed to compute i…
Fast and flexible inference for joint models of multivariate longitudinal and survival data using Integrated Nested Laplace Approximations
Denis Rustand, Janet van Niekerk, Elias Teixeira Krainski +2
Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quanti…
Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints
Cécile Proust-Lima, Tiphaine Saulnier, Viviane Philipps +5
Neurodegenerative diseases are characterized by numerous markers of progression and clinical endpoints. For instance, Multiple System Atrophy (MSA), a rare neurodegenerative synucl…
Joint models for the longitudinal analysis of measurement scales in the presence of informative dropout
Tiphaine Saulnier, Viviane Philipps, Wassilios G Meissner +4
In health cohort studies, repeated measures of markers are often used to describe the natural history of a disease. Joint models allow to study their evolution by taking into accou…
Modeling repeated self-reported outcome data: a continuous-time longitudinal Item Response Theory model
Cécile Proust-Lima, Viviane Philipps, Bastien Perrot +2
Item Response Theory (IRT) models have received growing interest in health science for analyzing latent constructs such as depression, anxiety, quality of life, or cognitive functi…
A multistate approach for mediation analysis in the presence of semi-competing risks with application in cancer survival disparities
Linda Valeri, Cécile Proust-Lima, Weijia Fan +2
We propose a novel methodology to quantify the effect of stochastic interventions on non-terminal time-to-events that lie on the pathway between an exposure and a terminal time-to-…
Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach
Anthony Devaux, Robin Genuer, Karine Pérès +1
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 stra…
Robust and Efficient Optimization Using a Marquardt-Levenberg Algorithm with R Package marqLevAlg
Viviane Philipps, Boris P Hejblum, Mélanie Prague +2
Implementations in R of classical general-purpose algorithms for local optimization generally have two major limitations which cause difficulties in applications to complex problem…
Time-varying exposure history and subsequent health outcomes: a two-stage approach to identify critical windows
Maude Wagner, Francine Grodstein, Karen Leffondre +2
Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk fa…
Dynamic Modelling of Multivariate Dimensions and Their Temporal Relationships using Latent Processes: Application to Alzheimer's Disease
Bachirou O. Taddé, Hélène Jacqmin-Gadda, Jean-François Dartigues +2
Alzheimer's disease gradually affects several components including the cerebral dimension with brain atrophies, the cognitive dimension with a decline in various functions and the…
A joint model for multiple dynamic processes and clinical endpoints: application to Alzheimer's disease
Cécile Proust-Lima, Viviane Philipps, Jean-François Dartigues
As other neurodegenerative diseases, Alzheimer's disease, the most frequent dementia in the elderly, is characterized by multiple progressive impairments in the brain structure and…
Individual dynamic predictions using landmarking and joint modelling: validation of estimators and robustness assessment
Loïc Ferrer, Hein Putter, Cécile Proust-Lima
After the diagnosis of a disease, one major objective is to predict cumulative probabilities of events such as clinical relapse or death from the individual information collected u…
Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer
Loïc Ferrer, Virginie Rondeau, James J. Dignam +3
Joint modelling of longitudinal and survival data is increasingly used in clinical trials on cancer. In prostate cancer for example, these models permit to account for the link bet…
Joint latent class model for longitudinal data and interval-censored semi-competing events: Application to dementia
Anaïs Rouanet, Pierre Joly, Jean-François Dartigues +2
Joint models are used in ageing studies to investigate the association between longitudinal markers and a time-to-event, and have been extended to multiple markers and/or competing…
Estimation of extended mixed models using latent classes and latent processes: the R package lcmm
Cécile Proust-Lima, Viviane Philipps, Benoit Liquet
The R package lcmm provides a series of functions to estimate statistical models based on linear mixed model theory. It includes the estimation of mixed models and latent class mix…
Joint modelling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach
Cécile Proust-Lima, Jean-François Dartigues, Hélène Jacqmin-Gadda
Joint models initially dedicated to a single longitudinal marker and a single time-to-event need to be extended to account for the rich longitudinal data of cohort studies. Multipl…
A universal approximate cross-validation criterion and its asymptotic distribution
Daniel Commenges, Cécile Proust-Lima, Cécilia Samieri +1
A general framework is that the estimators of a distribution are obtained by minimizing a function (the estimating function) and they are assessed through another function (the ass…