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Score de propension en grande dimension et régression pénalisée pour la détection automatisée de signaux en pharmacovigilance

Abstract : Post-marketing pharmacovigilance aims to detect as early as possible adverse effects of marketed drugs. It relies on large databases of individual case safety reports of adverse events suspected to be drug-induced. Several automated signal detection tools have been developed to mine these large amounts of data in order to highlight suspicious adverse event-drug combinations. Classical signal detection methods are based on disproportionality analyses of counts aggregating patients’ reports. Recently, multiple regression-based methods have been proposed to account for multiple drug exposures. In chapter 2, we propose a signal detection method based on the high-dimensional propensity score (HDPS). An empirical study, conducted on the French pharmacovigilance database with a reference signal set pertaining to drug-induced liver injury (DILIrank), is carried out to compare the performance of this method (in 12 modalities) to methods based on lasso penalized regressions. In this work, the influence of the score estimation method is minimal, unlike the score integration method. In particular, HDPS weighting with matching weights shows good performances, comparable to those of lasso-based methods. In chapter 3, we propose a method based on a lasso extension: the adaptive lasso which allows to introduce specific penalties to each variable through adaptive weights. We propose two new weights adapted to spontaneous reports data, as well as the use of the BIC for the choice of the penalty term. An extensive simulation study is performed to compare the performances of our proposals with other implementations of the adaptive lasso, a disproportionality method, lasso-based methods and HDPS-based methods. The proposed methods show overall better results in terms of false discoveries and sensitivity than competing methods. An empirical study similar to the one conducted in chapter 2 completes the evaluation. All the evaluated methods are implemented in the R package "adapt4pv" available on the CRAN. Alongside to methodological developments in spontaneous reporting, there has been a growing interest in the use of medico-administrative databases for signal detection in pharmacovigilance. Methodological research efforts in this area are to be developed. In chapter 4, we explore detection strategies exploiting spontaneous reports and the national health insurance permanent sample (Echantillon Généraliste des bénéficiaires, EGB). We first evaluate the performance of a detection on the EGB using DILIrank. Then, we consider a detection conducted on spontaneous reports based on an adaptive lasso integrating, through weights, the information related to the drug exposure of a control group measured in the EGB. In both cases, the contribution of medico-administrative data is difficult to evaluate because of the relatively small size of the EGB.
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Submitted on : Friday, April 2, 2021 - 7:21:13 PM
Last modification on : Monday, April 12, 2021 - 3:50:01 PM


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  • HAL Id : tel-03189182, version 1



Émeline Courtois. Score de propension en grande dimension et régression pénalisée pour la détection automatisée de signaux en pharmacovigilance. Pharmacologie. Université Paris-Saclay, 2020. Français. ⟨NNT : 2020UPASR009⟩. ⟨tel-03189182⟩



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