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ConvSCCS: convolutional self-controlled case-seris model for lagged adverser event detection

Morel, Maryan; Bacry, Emmanuel; Gaïffas, Stéphane; Guilloux, Agathe; Leroy, Fanny (2019), ConvSCCS: convolutional self-controlled case-seris model for lagged adverser event detection, Biostatistics. 10.1093/biostatistics/kxz003

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1712.08243.pdf (378.1Kb)
Type
Article accepté pour publication ou publié
Date
2019
Journal name
Biostatistics
Publication identifier
10.1093/biostatistics/kxz003
Metadata
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Author(s)
Morel, Maryan
Centre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
Bacry, Emmanuel cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Gaïffas, Stéphane
Laboratoire de Probabilités et Modèles Aléatoires [LPMA]
Guilloux, Agathe
Centre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
Leroy, Fanny
Caisse Nationale d'Assurance Maladie
Abstract (EN)
With the increased availability of large electronic health records databases comes the chance of enhancing health risks screening. Most post-marketing detection of adverse drug reaction (ADR) relies on physicians' spontaneous reports, leading to under-reporting. To take up this challenge, we develop a scalable model to estimate the effect of multiple longitudinal features (drug exposures) on a rare longitudinal outcome. Our procedure is based on a conditional Poisson regression model also known as self-controlled case series (SCCS). To overcome the need of precise risk periods specification, we model the intensity of outcomes using a convolution between exposures and step functions, which are penalized using a combination of group-Lasso and total-variation. Up to our knowledge, this is the first SCCS model with flexible intensity able to handle multiple longitudinal features in a single model. We show that this approach improves the state-of-the-art in terms of mean absolute error and computation time for the estimation of relative risks on simulated data. We apply this method on an ADR detection problem, using a cohort of diabetic patients extracted from the large French national health insurance database (SNIIRAM), a claims database containing medical reimbursements of more than 53 million people. This work has been done in the context of a research partnership between Ecole Polytechnique and CNAMTS (in charge of SNIIRAM).
Subjects / Keywords
Conditional Poisson model; Penalization; Risk screening; Scalability; Self-controlled case series; Total variation

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