Class-imbalanced subsampling lasso algorithm for discovering adverse drug reactions.
Background All methods routinely used to generate safety signals from pharmacovigilance databases rely on disproportionality analyses of counts aggregating patients' spontaneous reports. Recently, it was proposed to analyze individual spontaneous reports directly using Bayesian lasso logistic regres...
| Publicado en: | Statistical Methods in Medical Research Vol. 27; no. 3; pp. 785 - 798 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Sage Publications Inc.
Mar2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128167859&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128167859 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Mar2018 vid: 27 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 128167859 128167859 NLM27114328 128167859 10.1177/0962280216643116 NLM27114328 128167859 ppf: 785 ppct: 13 formats: tig: atl: Class-imbalanced subsampling lasso algorithm for discovering adverse drug reactions. aug: au: Ahmed, Ismaïl Pariente, Antoine Tubert-Bitter, Pascale affil: Inserm UMR 1181, Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases (B2PHI), F-94807 Villejuif, France sug: subj: Pharmacovigilance Probability Adverse Drug Event Algorithms Databases Statistics Methods Computer Simulation Logistic Regression Human France Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Background All methods routinely used to generate safety signals from pharmacovigilance databases rely on disproportionality analyses of counts aggregating patients' spontaneous reports. Recently, it was proposed to analyze individual spontaneous reports directly using Bayesian lasso logistic regressions. Nevertheless, this raises the issue of choosing an adequate regularization parameter in a variable selection framework while accounting for computational constraints due to the high dimension of the data. Purpose Our main objective is to propose a method, which exploits the subsampling idea from Stability Selection, a variable selection procedure combining subsampling with a high-dimensional selection algorithm, and adapts it to the specificities of the spontaneous reporting data, the latter being characterized by their large size, their binary nature and their sparsity. Materials and method Given the large imbalance existing between the presence and absence of a given adverse event, we propose an alternative subsampling scheme to that of Stability Selection resulting in an over-representation of the minority class and a drastic reduction in the number of observations in each subsample. Simulations are used to help define the detection threshold as regards the average proportion of false signals. They are also used to compare the performances of the proposed sampling scheme with that originally proposed for Stability Selection. Finally, we compare the proposed method to the gamma Poisson shrinker, a disproportionality method, and to a lasso logistic regression approach through an empirical study conducted on the French national pharmacovigilance database and two sets of reference signals. Results Simulations show that the proposed sampling strategy performs better in terms of false discoveries and is faster than the equiprobable sampling of Stability Selection. The empirical evaluation illustrates the better performances of the proposed method compared with gamma Poisson shrinker and the lasso in terms of number of reference signals retrieved. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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