New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection

Abstract Background Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these lar...

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Autores principales:	Émeline Courtois, Pascale Tubert-Bitter, Ismaïl Ahmed
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Adaptive logistic lasso BIC Variable selection Drug safety signal Spontaneous reporting Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/c48178560ced4b9e9749bf9fe6cab9a3
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spelling	oai:doaj.org-article:c48178560ced4b9e9749bf9fe6cab9a32021-12-05T12:24:46ZNew adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection10.1186/s12874-021-01450-31471-2288https://doaj.org/article/c48178560ced4b9e9749bf9fe6cab9a32021-12-01T00:00:00Zhttps://doi.org/10.1186/s12874-021-01450-3https://doaj.org/toc/1471-2288Abstract Background Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results. Methods We propose a new signal detection methodology based on the adaptive lasso. We derived two new adaptive weights from (i) a lasso regression using the Bayesian Information Criterion (BIC), and (ii) the class-imbalanced subsampling lasso (CISL), an extension of stability selection. The BIC is used in the adaptive lasso stage for variable selection. We performed an extensive simulation study and an application to real data, where we compared our methods to the existing adaptive lasso, and recent detection approaches based on lasso regression or propensity scores in high dimension. For both studies, we evaluate the methods in terms of false discoveries and sensitivity. Results In the simulations and the application, both proposed adaptive weights show equivalent or better performances than the other competitors, with an advantage for the CISL-based adaptive weights. CISL and lasso regression using BIC are solid alternatives. Conclusion Our proposed adaptive lasso is an appealing methodology for signal detection in pharmacovigilance. Although we cannot rely on test theory, our approaches show a low and stable False Discovery Rate in all simulation settings. All methods evaluated in this work are implemented in the adapt4pv R package.Émeline CourtoisPascale Tubert-BitterIsmaïl AhmedBMCarticleAdaptive logistic lassoBICVariable selectionDrug safety signalSpontaneous reportingMedicine (General)R5-920ENBMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Adaptive logistic lasso BIC Variable selection Drug safety signal Spontaneous reporting Medicine (General) R5-920
spellingShingle	Adaptive logistic lasso BIC Variable selection Drug safety signal Spontaneous reporting Medicine (General) R5-920 Émeline Courtois Pascale Tubert-Bitter Ismaïl Ahmed New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection
description	Abstract Background Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results. Methods We propose a new signal detection methodology based on the adaptive lasso. We derived two new adaptive weights from (i) a lasso regression using the Bayesian Information Criterion (BIC), and (ii) the class-imbalanced subsampling lasso (CISL), an extension of stability selection. The BIC is used in the adaptive lasso stage for variable selection. We performed an extensive simulation study and an application to real data, where we compared our methods to the existing adaptive lasso, and recent detection approaches based on lasso regression or propensity scores in high dimension. For both studies, we evaluate the methods in terms of false discoveries and sensitivity. Results In the simulations and the application, both proposed adaptive weights show equivalent or better performances than the other competitors, with an advantage for the CISL-based adaptive weights. CISL and lasso regression using BIC are solid alternatives. Conclusion Our proposed adaptive lasso is an appealing methodology for signal detection in pharmacovigilance. Although we cannot rely on test theory, our approaches show a low and stable False Discovery Rate in all simulation settings. All methods evaluated in this work are implemented in the adapt4pv R package.
format	article
author	Émeline Courtois Pascale Tubert-Bitter Ismaïl Ahmed
author_facet	Émeline Courtois Pascale Tubert-Bitter Ismaïl Ahmed
author_sort	Émeline Courtois
title	New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection
title_short	New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection
title_full	New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection
title_fullStr	New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection
title_full_unstemmed	New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection
title_sort	new adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection
publisher	BMC
publishDate	2021
url	https://doaj.org/article/c48178560ced4b9e9749bf9fe6cab9a3
work_keys_str_mv	AT emelinecourtois newadaptivelassoapproachesforvariableselectioninautomatedpharmacovigilancesignaldetection AT pascaletubertbitter newadaptivelassoapproachesforvariableselectioninautomatedpharmacovigilancesignaldetection AT ismailahmed newadaptivelassoapproachesforvariableselectioninautomatedpharmacovigilancesignaldetection
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New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection

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