Paediatric pharmacovigilance: use of pharmacovigilance data mining algorithms for signal detection in a safety dataset of a paediatric clinical study conducted in seven African countries.
- Citation data:
PloS one, ISSN: 1932-6203, Vol: 9, Issue: 5, Page: e96388
- Publication Year:
- 10.1371/journal.pone.0096388; 10.1371/journal.pone.0096388.g001; 10.1371/journal.pone.0096388.t002; 10.1371/journal.pone.0096388.t001
- PMC4006882; 4006882
- Biochemistry, Genetics and Molecular Biology; Agricultural and Biological Sciences; Biological Sciences; organisms; protozoans; Parasitic protozoans; Malarial parasites; Plasmodium falciparum; Information technology; data mining; signal processing; Clinical medicine; clinical trials; Phase III clinical investigation; Phase IV clinical investigation; epidemiology; Pharmacoepidemiology; health care; Health care policy; Drug policy; Infectious diseases; Parasitic diseases; malaria; pharmacology; Drug research and development; Drug marketing; Adverse reactions; Public and occupational health; mathematics; Statistics (mathematics); Statistical methods; research design; Clinical research design; adverse; event-drug; drug-event; combinations; prr; bcpnn; suspected; signals; mining
Pharmacovigilance programmes monitor and help ensuring the safe use of medicines which is critical to the success of public health programmes. The commonest method used for discovering previously unknown safety risks is spontaneous notifications. In this study we examine the use of data mining algorithms to identify signals from adverse events reported in a phase IIIb/IV clinical trial evaluating the efficacy and safety of several Artemisinin-based combination therapies (ACTs) for treatment of uncomplicated malaria in African children.