SEGALAS, Corentin; HELMER, Catherine; GENUER, Robin; PROUST LIMA, Cecile

doi:10.1002/sim.10214

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

HELMER, Catherine

Bordeaux population health [BPH]

GENUER, Robin

Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

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Article de revue

Ce document a été publié dans

Statistics in Medicine. 2024-09-09

Résumé en anglais

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.< Réduire

Mots clés en anglais

Functional Principal Component Analysis

Missing Data

Mixed Models

Sparse Functional Data

URI

https://oskar-bordeaux.fr/handle/20.500.12278/201732

DOI

http://dx.doi.org/10.1002/sim.10214

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219

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

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Résumé en anglais

Mots clés en anglais

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Unités de recherche