scholarly journals Sensitivity of Quantitative Signal Detection in Regards to Pharmacological Neuroenhancement

2017 ◽  
Vol 18 (1) ◽  
pp. 101 ◽  
Author(s):  
Maximilian Gahr ◽  
Bernhard Connemann ◽  
Carlos Schönfeldt-Lecuona ◽  
René Zeiss
Keyword(s):  
Signal Detection ◽  
Quantitative Signal Detection

Quantitative Signal Detection and Analysis in Pharmacovigilance

2014 ◽  
pp. 331-354 ◽  
Author(s):  
Andrew Bate ◽  
Antoine Pariente ◽  
Manfred Hauben ◽  
Bernard Bégaud
Keyword(s):  
Signal Detection ◽  
Quantitative Signal Detection

From Qualitative to Quantitative Signal Detection

Drug Safety ◽  
2006 ◽  
Vol 29 (10) ◽  
pp. 911-1010
Author(s):  
R. Benkirane ◽  
R. Soulaymani ◽  
H. Kourrad
Keyword(s):  
Signal Detection ◽  
Quantitative Signal Detection

Association between haemorrhages and treatment with selective and non-selective serotonergic antidepressants: Possible implications of quantitative signal detection

2015 ◽  
Vol 229 (1-2) ◽  
pp. 257-263 ◽  
Author(s):  
Maximilian Gahr ◽  
René Zeiss ◽  
Dirk Lang ◽  
Bernhard J. Connemann ◽  
Christoph Hiemke ◽  
...  
Keyword(s):  
Signal Detection ◽  
Quantitative Signal Detection

scholarly journals Evaluation of quantitative signal detection in EudraVigilance for orphan drugs: possible risk of false negatives

2019 ◽  
Vol 10 ◽  
pp. 204209861988281 ◽  
Author(s):  
Marco Sardella ◽  
Calin Lungu
Keyword(s):  
Signal Detection ◽  
Safety Profile ◽  
Safety Data ◽  
Orphan Drugs ◽  
Drug Event ◽  
Confounding Factors ◽  
Medical Conditions ◽  
False Negatives ◽  
Quantitative Signal Detection ◽  
Wide Range

Different strategies have been studied to allow a better characterization of the safety profile of orphan drugs soon after their approval. At the end of the development phases only few data are available because of the small number of subjects exposed to an orphan medicine for the treatment of rare or ultra-rare conditions. As a consequence, the evaluation of the safety profile is limited at the time of the first approval. In the post-marketing period, all available sources should be combined for a better understanding of the safety of orphan drugs. These sources, include outputs from large databases such as the European Medicines Agency’s EudraVigilance database. Analyses of data from this source are required to be performed by marketing authorization holders (MAHs) as part of their signal management activities. In 2018, the Pharmacovigilance Risk Assessment Committee (PRAC) assessed 114 confirmed signals, 79% of which included data from EudraVigilance. MAHs have access to statistical calculations for drug–event combinations (DECs) from EudraVigilance, provided in the form of measures of disproportionality of ratios of the observed proportion of spontaneous cases for a DEC in relation to the proportion of cases that would be expected if no association existed between the drug and the event. However, such statistical summaries for orphan drugs could be misleading because of the very limited safety data available for orphan drugs (under-reporting together with low numbers of exposed patients). In addition, the applied statistical methodology in most instances is constrained by different confounding factors such as indications of specific medicines and the wide spectrum of medical conditions/diseases of patients from whom reporting of disproportionality ratios are derived (i.e. proportions of DECs for orphan drugs (ODECs) from a small patient population suffering the rare disease and the proportion of DECs in the rest of the population represented in the whole database who have been treated with other medicines for a wide range of indications, and prescribed to treat completely different medical conditions). As expected, these statistical calculations produced not only signals of disproportionate reporting (SDRs) that are false positives, but also not sensitive enough to detect certain SDRs, thus resulting in false negatives. In the context of rare/ultra-rare life-threatening diseases where new molecules have been made available on the market on the basis of their proven efficacy, but with only limited safety data at the time of approval, false negatives could be a special concern since unlikely converted in positives or becoming positives with notable delay. Subgroup analyses (using a limited dataset comprising ADRs within specific individual case safety reports (ICSRs), sorted by indication/disease relevant to the drug of interest could, at least in part, possibly reduce some of the weaknesses resulting from the abovementioned confounding factors. On the other hand it could also cause the loss of some identification of SDRs that would be captured if no database restrictions had been undertaken. Therefore, data subgroup analysis should not be selected as a preferred approach to quantitative signal detection for orphan drugs but rather evaluated as complementary possibly to confirm negatives or to further characterize detected SDRs. Some examples of false negatives originating from quantitative signal detection in EudraVigilance applied to orphan drugs are discussed in this article.

Hepatotoxicity related to anti-depressive psychopharmacotherapy: Implications of quantitative signal detection

2017 ◽  
Vol 41 (S1) ◽  
pp. S756-S756
Author(s):  
M. Gahr ◽  
R. Zeiss ◽  
D. Lang ◽  
B.J. Connemann ◽  
C. Schönfeldt-Lecuona
Keyword(s):  
Signal Detection ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Detection Analysis ◽  
Quantitative Signal Detection ◽  
Reaction Data ◽  
Signal Detection Analysis ◽  
Competing Interest ◽  
Uppsala Monitoring ◽  
Positive Controls

IntroductionDrug-induced liver injury is a major problem of pharmacotherapy and is also frequent with anti-depressive psychopharmacotherapy.Objectives/aimsHowever, there are only few studies using a consistent methodologic approach to study hepatotoxicity of a larger group of antidepressants.MethodsWe performed a quantitative signal detection analysis using pharmacovigilance data from the Uppsala monitoring center from the WHO that records adverse drug reaction data from worldwide sources; we calculated reporting odds ratios (ROR) as measures for disproportionality within a case-/non-case approach for several frequently prescribed anti-depressants.ResultsBoth positive controls, amineptine (ROR 38.4 [95% CI: 33.8–43.6]) and nefazodone (ROR 3.2 [95% CI: 3.0–3.5]), were statistically associated with hepatotoxicity. Following amineptine, agomelatine (ROR 6.4 [95% CI: 5.7–7.2]) was associated with the second highest ROR, followed by tianeptine (ROR 4.4 [95% CI: 3.6–5.3]), mianserin (ROR 3.6 [95% CI: 3.3–3.4]) and nefazodone.ConclusionsIn line with previous studies our results support the hypothesis that agomelatine and several other anti-depressants may be associated with relevant hepatotoxicity. However, the used data and applied method do not allow a quantitative evaluation of hepatotoxicity or assessment of substance–specific differences regarding the extent of hepatotoxicity.Disclosure of interestThe authors have not supplied their declaration of competing interest.

A web-based quantitative signal detection system on adverse drug reaction in China

2009 ◽  
Vol 65 (7) ◽  
pp. 729-741 ◽  
Author(s):  
Chanjuan Li ◽  
Jielai Xia ◽  
Jianxiong Deng ◽  
Wenge Chen ◽  
Suzhen Wang ◽  
...  
Keyword(s):  
Adverse Drug Reaction ◽  
Drug Reaction ◽  
Signal Detection ◽  
Detection System ◽  
Web Based ◽  
Quantitative Signal Detection
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