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Journal articles on the topic 'Signal detection in pharmacovigilance'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Kumar, Anoop, and Henna Khan. "Signal Detection and their Assessment in Pharmacovigilance." Open Pharmaceutical Sciences Journal 2, no. 1 (2015): 66–73. http://dx.doi.org/10.2174/1874844901502010066.

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Signal detection and its assessment is the most important aspect in pharmacovigilance which plays a key role in ensuring that patients receive safe drugs. For detection of adverse drug reactions, clinical trials usually provide limited information as they are conducted under strictly controlled conditions. Some of the adverse drug reactions can be detected only after long term use in larger population and in specific patient groups due to specific concomitant medications or disease. The detection of unknown and unexpected safety signals as early as possible from post marketing data is one of t

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V. Sriramakrishnan, G., M. Muthu Selvam, K. Mariappan, and G. Suseendran. "Pharmacovigilance, signal detection using statistical data mining methods." International Journal of Engineering & Technology 7, no. 2.31 (2018): 122. http://dx.doi.org/10.14419/ijet.v7i2.31.13423.

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Pharmacovigilance programmes monitor and help safeguarding the use of medicines which is grave to the success of public health programmes. Identifying new possible risks and developing risk minimization action plans to prevent or ease these risks is at the heart of all pharmacovigilance activities throughout the product lifecycle. In this paper we examine the use of data mining algorithms to identify signals from adverse events reported. The capabilities include screening, data mining and frequency tabulation for potential signals, including signal estimation using established statistical sign

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Rani, Ritu, Subhash Chand, Arjun Singh, Deovrat Kumar, and Meena Devi. "A Review on Signal in Pharmacovigilance." Asian Journal of Pharmaceutical Research and Development 7, no. 6 (2019): 80–84. http://dx.doi.org/10.22270/ajprd.v7i6.591.

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A safety signal is data or information that may suggest a new causal association, or contribute new information about a known association, between a medicine and an adverse event that justifies further investigation. Signals are generated from several sources such as spontaneous reports, clinical studies and the scientific literature. Signal is a potential medicine safety issues, which are derived from individual case safety reports in vigiBase. Signal detection is a cornerstone of drug development process ensuring drug safety. The early detection of Signals helps to improve patient safety and

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Meyboom, Ronald H. B., Antoine C. G. Egberts, I. Ralph Edwards, Yechiel A. Hekster, Fred H. P. de Koning, and Frank W. J. Gribnau. "Principles of Signal Detection in Pharmacovigilance." Drug Safety 16, no. 6 (1997): 355–65. http://dx.doi.org/10.2165/00002018-199716060-00002.

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Shakir, Saad A. W. "Thoughts on Signal Detection in Pharmacovigilance." Drug Safety 30, no. 7 (2007): 603–6. http://dx.doi.org/10.2165/00002018-200730070-00005.

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6

Noguchi, Yoshihiro, Tomoya Tachi, and Hitomi Teramachi. "Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database." Pharmaceutics 12, no. 8 (2020): 762. http://dx.doi.org/10.3390/pharmaceutics12080762.

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Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Even

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Lee, Seung-Mi, Seokyung Hahn, and Byung-Joo Park. "Signal Detection and Causality Evaluation for Pharmacovigilance." Journal of Korean Society for Clinical Pharmacology and Therapeutics 13, no. 2 (2005): 121. http://dx.doi.org/10.12793/jkscpt.2005.13.2.121.

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Sorbello, Alfred, Anna Ripple, Joseph Tonning, et al. "Harnessing scientific literature reports for pharmacovigilance." Applied Clinical Informatics 26, no. 01 (2017): 291–305. http://dx.doi.org/10.4338/aci-2016-11-ra-0188.

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Summary Objectives: We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers’ capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. Methods: A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We

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Sharma, Rohit, Jayram Hazra, Rohit K. Ravte, and Arunabh Tripathi. "Pharmacovigilance in Ayurveda: Statistical Input for Signal Detection." Journal of Drug Research in Ayurvedic Sciences 4, no. 1 (2019): 33–38. http://dx.doi.org/10.5005/jdras-10059-0061.

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10

Meyboom, R. H. B., A. C. G. Egberts, and I. R. Edwards. "Erratum to: Principles of signal detection in pharmacovigilance." Drug Safety 17, no. 2 (1997): 118. http://dx.doi.org/10.1007/bf03257469.

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11

Chakraborty, BhaswatS. "Pharmacovigilance: A data mining approach to signal detection." Indian Journal of Pharmacology 47, no. 3 (2015): 241. http://dx.doi.org/10.4103/0253-7613.157102.

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12

Vilar, S., PB Ryan, D. Madigan, et al. "Similarity-Based Modeling Applied to Signal Detection in Pharmacovigilance." CPT: Pharmacometrics & Systems Pharmacology 3, no. 9 (2014): 137. http://dx.doi.org/10.1038/psp.2014.35.

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13

Malikova, Marina A. "Practical applications of regulatory requirements for signal detection and communications in pharmacovigilance." Therapeutic Advances in Drug Safety 11 (January 2020): 204209862090961. http://dx.doi.org/10.1177/2042098620909614.

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Pharmacovigilance is a field where communication is crucial, and exchange of information is expected to be done in a timely manner. Information from individual case reports is transmitted from pharmaceutical industry and health professionals to the regulatory authorities. The safety profile of a drug is established by analyzing individual cases and aggregate reports. The cumulative information, obtained from these reports, can be used to assist pharmacovigilance professionals in the detection of potential safety signals by monitoring evolving trends. If there is a message identifying concern a

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Rees, Sue, Sadiqa Mian, and Neal Grabowski. "Using social media in safety signal management: is it reliable?" Therapeutic Advances in Drug Safety 9, no. 10 (2018): 591–99. http://dx.doi.org/10.1177/2042098618789596.

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Social media use is growing globally, with a reported 3 billion active users in 2017. This medium is used increasingly in a health setting by patients (and to a limited extent, healthcare professionals) to share experiences and ask advice on medical conditions as well as pharmaceutical products. In recent years, attention has turned to this huge, generally untapped, source of potential health information as a possible tool for pharmacovigilance, and in particular signal detection. In this article we explore some of the challenges of utilizing social media for safety signal detection and look a

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Ahmed, I., C. Dalmasso, F. Haramburu, F. Thiessard, P. Broët, and P. Tubert-Bitter. "False Discovery Rate Estimation for Frequentist Pharmacovigilance Signal Detection Methods." Biometrics 66, no. 1 (2009): 301–9. http://dx.doi.org/10.1111/j.1541-0420.2009.01262.x.

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16

Chan, K. Arnold, and Manfred Hauben. "Signal detection in pharmacovigilance: empirical evaluation of data mining tools." Pharmacoepidemiology and Drug Safety 14, no. 9 (2005): 597–99. http://dx.doi.org/10.1002/pds.1128.

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17

Patil, Dr Rajrajeshwari R., and Dr Vivek Singh. "One size does not fit all: a summary of signal detection methods." Journal of Pharmacovigilance and Drug Research 2, no. 3 (2021): 4–6. http://dx.doi.org/10.53411/jpadr.2021.2.3.2.

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The selection of an appropriate signal detection method is pivotal in the identification process of safety signals in pharmacovigilance. Nevertheless, the early detection of safety signals is even more important to prevent the occurrence of another thalidomide tragedy in humans. Spontaneous reports, follow-up studies, scientific literature, preclinical & clinical studies, are valuable sources of adverse events; but on the other hand, these reported adverse events are extremely diverse, hence comprehending this can result in formulating the right signal detection and evaluation strategies.

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18

Dogra, Sunil, Samir Malhotra, Promila Pandhi, Sharonjeet Kaur, and Sujit Rajagopalan. "A Case of Drug-induced Toxic Epidermal Necrolysis: Pharmacovigilance in Action and Lessons to learn." Journal of Postgraduate Medicine, Education and Research 46, no. 1 (2012): 40–42. http://dx.doi.org/10.5005/jp-journals-10028-1010.

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ABSTRACT Pharmacovigilance refers to the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problems. During the pharmacovigilance activities undertaken by us, a case of toxic epidermal necrolysis provided us with a setting for discussing various aspects of pharmacovigilance—the process itself, important signal generators that it may yield for practice, research and policy-related matters. How to cite this article Kaur S, Rajagopalan S, Shafiq N, Dogra S, Pandhi P, Malhotra S. A Case of Drug-induced Toxic Epid

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19

Kumaraguru Anbalagan and Shanmugasundaram P. "A non-randomized interventional study to promote the knowledge, attitude and practices of community pharmacists towards pharmacovigilance." International Journal of Research in Pharmaceutical Sciences 10, no. 4 (2019): 3286–92. http://dx.doi.org/10.26452/ijrps.v10i4.1634.

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Pharmacovigilance promotes the safe and effective use of medicines and thereby optimizes the treatment quality. However, lack of awareness among community pharmacists towards pharmacovigilance decreases the proportion of adverse drug reactions reported and impairs the signal detection process. Hence this study was designed to assess and promote the awareness and attitude of community pharmacists towards pharmacovigilance. This educational interventional study was carried out with 102 community pharmacists across Chennai. A pre-validated three domain-containing questionnaire, 20 items was used

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20

Choi, Jung-Yoon, Jae-Hee Choi, Myeong-Gyu Kim, and Sandy-Jeong Rhie. "Signal Detection of Adverse Drug Reactions of Cephalosporins Using Data from a National Pharmacovigilance Database." Pharmaceuticals 14, no. 5 (2021): 425. http://dx.doi.org/10.3390/ph14050425.

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This case-non-case study aims to detect signals not currently listed on cephalosporin drug labels. From 2009 to 2018, adverse event (AE) reports concerning antibacterial drugs (anatomical therapeutic chemical (ATC) code J01) in the Korea Adverse Events Reporting System (KAERS) database were examined. For signal detection, three indices of disproportionality, proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC), were calculated. The list of signals was compared with ADRs on the drug labels from the United States, United Kingdom, Japan, and South Korea.

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21

Ahmed, Ismaïl, Françoise Haramburu, Annie Fourrier-Réglat, et al. "Bayesian pharmacovigilance signal detection methods revisited in a multiple comparison setting." Statistics in Medicine 28, no. 13 (2009): 1774–92. http://dx.doi.org/10.1002/sim.3586.

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22

Osborne, Vicki, and Saad A. W. Shakir. "The 9th Biennial Conference on Signal Detection and Interpretation in Pharmacovigilance." Drug Safety 41, no. 1 (2017): 139–41. http://dx.doi.org/10.1007/s40264-017-0587-1.

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23

Hui, Tom Z. "Integrating Regulatory Drug Label Information to Facilitate Evaluation of Adverse Events in Pharmacovigilance." Current Drug Safety 15, no. 2 (2020): 124–30. http://dx.doi.org/10.2174/1574886315666200224101011.

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Background: Efficiency and accuracy for signal detection and evaluation activities are integral components of routine Pharmacovigilance (PV) practices. However, an Individual Case Safety Report (ICSR) may consist of a variety of confounders such as Concomitant Medications (CM), Past Medical History (PMH), and concurrent medical conditions that influence a safety officer’s evaluation of a potential Adverse Event (AE). Limited pharmacovigilance systems are currently available as a tool designed to enhance the efficiency and accuracy of signal detection and management. Objective: To introduce a s

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24

Hauben, Manfred, Eric Hung, Jennifer Wood, Amit Soitkar, and Daniel Reshef. "The impact of database restriction on pharmacovigilance signal detection of selected cancer therapies." Therapeutic Advances in Drug Safety 8, no. 5 (2017): 145–56. http://dx.doi.org/10.1177/2042098616685010.

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Background: The aim of this study was to investigate whether database restriction can improve oncology drug pharmacovigilance signal detection performance. Methods: We used spontaneous adverse event (AE) reports in the United States (US) Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Positive control (PC) drug medical concept (DMC) pairs were selected from safety information not included in the product’s first label but subsequently added as label changes. These medical concepts (MCs) were mapped to the Medical Dictionary for Regulatory Activities (MedDRA)

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25

Alomar, Muaed, Subish Palaian, and Moawia M. Al-tabakha. "Pharmacovigilance in perspective: drug withdrawals, data mining and policy implications." F1000Research 8 (December 16, 2019): 2109. http://dx.doi.org/10.12688/f1000research.21402.1.

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Considering that marketed drugs are not free from side effects, many countries have initiated pharmacovigilance programs. These initiatives have provided countries with methods of detection and prevention of adverse drug reactions at an earlier stage, thus preventing harm occurring in the larger population. In this review, examples of drug withdrawals due to effective pharmacovigilance programs have been provided with details. In addition, information concerning data mining in pharmacovigilance, an effective method to assess pharmacoepidemiologic data and detecting signals for rare and uncommo

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Kutekhova, G. V., E. O. Zhuravleva, M. A. Darmostukova, et al. "Signal Messages in Pediatric Practice." Safety and Risk of Pharmacotherapy 6, no. 4 (2018): 180–86. http://dx.doi.org/10.30895/2312-7821-2018-6-4-180-186.

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Abstract. Detection and analysis of drug safety signals in children is a mandatory part of pharmacovigilance. The criteria for evaluating signals of adverse effects were considered. Spontaneous reports databases as a source for detecting signaling information about adverse reactions, including in children were used. More than 17.5 million cases of adverse reactions identified in WHO global database of individual case safety reports — VigiBase which was detected since 1968 in 120 countries — members of the WHO Drug Treatment Control Program. Of these, 1.5 million cases of adverse reactions occu

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Bennett, C., B. J. Edwards, C. C. Tigue, et al. "Quality not quantity: Key success factors from the first decade of safety reports from the Research on Adverse Drug Events and Reports project (RADAR)." Journal of Clinical Oncology 27, no. 15_suppl (2009): 6559. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.6559.

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6559 Background: RADAR is the only independent academic pharmacovigilance organization funded exclusively by peer-reviewed grants. We describe the role of high quality case reports in the detection of drug safety signals. Methods: RADAR has identified 11 cancer-related adverse drug reactions (ADRs). Initial reports for small numbers of cases were obtained from our own institution, NU, (4 ADRs) or from referral centers (7 ADRs). Clinicians at these centers voluntarily provided brief case reports to RADAR, who submitted detailed case reports to the FDA/manufacturer. Clinicians were promised that

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Bate, Andrew, Ken Hornbuckle, Juhaeri Juhaeri, Stephen P. Motsko, and Robert F. Reynolds. "Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance." Therapeutic Advances in Drug Safety 10 (January 2019): 204209861986474. http://dx.doi.org/10.1177/2042098619864744.

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Koutkias, Vassilis G., and Marie-Christine Jaulent. "Computational Approaches for Pharmacovigilance Signal Detection: Toward Integrated and Semantically-Enriched Frameworks." Drug Safety 38, no. 3 (2015): 219–32. http://dx.doi.org/10.1007/s40264-015-0278-8.

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Hult, Sara, Daniele Sartori, Tomas Bergvall, et al. "A Feasibility Study of Drug–Drug Interaction Signal Detection in Regular Pharmacovigilance." Drug Safety 43, no. 8 (2020): 775–85. http://dx.doi.org/10.1007/s40264-020-00939-y.

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van Gaalen, Rolina D., Michal Abrahamowicz, and David L. Buckeridge. "The impact of exposure model misspecification on signal detection in prospective pharmacovigilance." Pharmacoepidemiology and Drug Safety 24, no. 5 (2014): 456–67. http://dx.doi.org/10.1002/pds.3700.

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Harpaz, R., W. DuMouchel, P. LePendu, A. Bauer-Mehren, P. Ryan, and N. H. Shah. "Performance of Pharmacovigilance Signal-Detection Algorithms for the FDA Adverse Event Reporting System." Clinical Pharmacology & Therapeutics 93, no. 6 (2013): 539–46. http://dx.doi.org/10.1038/clpt.2013.24.

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Trinh, Nhung T. H., Elodie Solé, and Mehdi Benkebil. "Benefits of combining change-point analysis with disproportionality analysis in pharmacovigilance signal detection." Pharmacoepidemiology and Drug Safety 28, no. 3 (2018): 370–76. http://dx.doi.org/10.1002/pds.4613.

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Lee, Suehyun, Jiyeob Choi, Hun-Sung Kim, et al. "Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records." Journal of the American Medical Informatics Association 24, no. 4 (2017): 697–708. http://dx.doi.org/10.1093/jamia/ocw168.

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Abstract Objective. We propose 2 Medical Dictionary for Regulatory Activities–enabled pharmacovigilance algorithms, MetaLAB and MetaNurse, powered by a per-year meta-analysis technique and improved subject sampling strategy. Matrials and methods. This study developed 2 novel algorithms, MetaLAB for laboratory abnormalities and MetaNurse for standard nursing statements, as significantly improved versions of our previous electronic health record (EHR)–based pharmacovigilance method, called CLEAR. Adverse drug reaction (ADR) signals from 117 laboratory abnormalities and 1357 standard nursing stat

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Yu, Yue, Kathryn Ruddy, Aaron Mansfield, et al. "Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study." JMIR Medical Informatics 8, no. 6 (2020): e17353. http://dx.doi.org/10.2196/17353.

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Background Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. Objective The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration–approved immune checkpoint inhibitors. Methods In our framework, we first used the Food and Drug Administration’s Adverse Event Reporting System (FAERS) standardized in an Observat

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Gupta, Amit, Sunil Verma, and Simranjeet Kaur. "Importance of Literature in Pharmacovigilance: A Review." Research in Pharmacy and Health Sciences 2, no. 1 (2016): 36–38. http://dx.doi.org/10.32463/rphs.2016.v02i01.07.

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Pharmacovigilance is to track and detect new adverse drug reactions mainly due to drugs or due to any other chemical substance or similar entity. The knowledge of a drug’s adverse reactions can be increased by various means, including spontaneous reporting, intensive monitoring and literature searching. But, in this review, we discuss how the medical literature plays a crucial role in pharmacovigilance. It is necessary to improve systematic reviews of adverse drug reactions. As literature is one of the vital sources of signal detection, it is essential for pharmaceutical companies to establish

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Pariente, A., M. Didailler, P. Avillach, et al. "Choice of the Comparator for Signal Generation in Pharmacovigilance Databases: Impact On Detection Thresholds." Drug Safety 30, no. 10 (2007): 919–90. http://dx.doi.org/10.2165/00002018-200730100-00015.

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Hauben, Manfred, Chen Zou, Ed Whalen, Wei Wang, and Li Hua Zhang. "A Pilot Study on the Feasibility of UsingP-Plots for Signal Detection in Pharmacovigilance." Statistics in Biopharmaceutical Research 7, no. 1 (2015): 25–35. http://dx.doi.org/10.1080/19466315.2014.1002628.

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Lee, Inyoung, Jeremy D. Jokinen, Stephanie Y. Crawford, Gregory S. Calip, Ryan D. Kilpatrick, and Todd A. Lee. "Exploring Completeness of Adverse Event Reports as a Tool for Signal Detection in Pharmacovigilance." Therapeutic Innovation & Regulatory Science 55, no. 1 (2020): 142–51. http://dx.doi.org/10.1007/s43441-020-00199-z.

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van Gaalen, Rolina D., Michal Abrahamowicz, and David L. Buckeridge. "Using Multiple Pharmacovigilance Models Improves the Timeliness of Signal Detection in Simulated Prospective Surveillance." Drug Safety 40, no. 11 (2017): 1119–29. http://dx.doi.org/10.1007/s40264-017-0555-9.

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Caster, Ola, Lovisa Sandberg, Tomas Bergvall, Sarah Watson, and G. Niklas Norén. "vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world use." Pharmacoepidemiology and Drug Safety 26, no. 8 (2017): 1006–10. http://dx.doi.org/10.1002/pds.4247.

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Hauben, Manfred. "A Brief Primer on Automated Signal Detection." Annals of Pharmacotherapy 37, no. 7-8 (2003): 1117–23. http://dx.doi.org/10.1345/aph.1c515.

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BACKGROUND: Statistical techniques have traditionally been underused in spontaneous reporting systems used for postmarketing surveillance of adverse drug events. Regulatory agencies, pharmaceutical companies, and drug monitoring centers have recently devoted considerable efforts to develop and implement computer-assisted automated signal detection methodologies that employ statistical theory to enhance screening efforts of expert clinical reviewers. OBJECTIVE: To provide a concise state-of-the-art review of the most commonly used automated signal detection procedures, including the underlying

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Shin, Hyunah, Jaehun Cha, Youngho Lee, Jong-Yeup Kim, and Suehyun Lee. "Real-world data-based adverse drug reactions detection from the Korea Adverse Event Reporting System databases with electronic health records-based detection algorithm." Health Informatics Journal 27, no. 3 (2021): 146045822110330. http://dx.doi.org/10.1177/14604582211033014.

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Pharmacovigilance involves monitoring of drugs and their adverse drug reactions (ADRs) and is essential for their safety post-marketing. Because of the different types and structures of medical databases, several previous surveillance studies have analyzed only one database. In the present study, we extracted potential drug–ADR pairs from electronic health record (EHR) data using the MetaNurse algorithm and analyzed them using the Korean Adverse Event Reporting System (KAERS) database for systematic validation. The Medical Dictionary for Regulatory Activities (MedDRA) and World Health Organiza

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Yuksel, Mustafa, Suat Gonul, Gokce Banu Laleci Erturkmen, et al. "An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies." BioMed Research International 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/6741418.

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Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while st

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Quattrini, G., A. Zambon, L. Simoni, and G. Fiori. "Disproportionality Measures Used in Signal Detection: An Assessment on Pharmacovigilance Adverse Event Reporting System Data." Value in Health 18, no. 7 (2015): A720. http://dx.doi.org/10.1016/j.jval.2015.09.2726.

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46

Pizzoglio, Véronique, Ismaïl Ahmed, Pascal Auriche, et al. "Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study." European Journal of Clinical Pharmacology 68, no. 5 (2011): 793–99. http://dx.doi.org/10.1007/s00228-011-1178-1.

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Caster, Ola, Kristina Juhlin, Sarah Watson, and G. Niklas Norén. "Improved Statistical Signal Detection in Pharmacovigilance by Combining Multiple Strength-of-Evidence Aspects in vigiRank." Drug Safety 37, no. 8 (2014): 617–28. http://dx.doi.org/10.1007/s40264-014-0204-5.

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48

Gahr, M., R. Zeiss, D. Lang, B. J. Connemann, and C. Schönfeldt-Lecuona. "Hepatotoxicity related to anti-depressive psychopharmacotherapy: Implications of quantitative signal detection." European Psychiatry 41, S1 (2017): S756. http://dx.doi.org/10.1016/j.eurpsy.2017.01.1414.

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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 appr

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Patadia, Vaishali K., Preciosa Coloma, Martijn J. Schuemie, et al. "Using real-world healthcare data for pharmacovigilance signal detection – the experience of the EU-ADR project." Expert Review of Clinical Pharmacology 8, no. 1 (2014): 95–102. http://dx.doi.org/10.1586/17512433.2015.992878.

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Yu, Yue, Kathryn J. Ruddy, Na Hong, et al. "ADEpedia-on-OHDSI: A next generation pharmacovigilance signal detection platform using the OHDSI common data model." Journal of Biomedical Informatics 91 (March 2019): 103119. http://dx.doi.org/10.1016/j.jbi.2019.103119.

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