Signal Detection for Medical Scientists_ Likelihood Ratio Test-based Methodology (2021)

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    Data mining is a trans-disciplinary field of science in which a cross-cutting group of scientists use computer technologies, statistical algorithms, and artificial intelligence to find patterns or clustering of outcomes of interest in large databases. Data mining for signal detection signal detection plays an important role in surveillance for medical products including drugs, biologics, and devices. More recent research has revealed that different data mining tools have varied strengths and weaknesses depending on the statistical assumptions underlying their analytical approach. Scientists have been investigating the use of data mining tools to identify drug-adverse event (AE) associations in large databases since 1998. 
    Several algorithms have been described to sort and quantitatively evaluate the strength of AE Adverse event/AE signals. Disproportionate analysis methods have often been used to identify AE signals in drug safety databases containing spontaneous reports. These methodologies classify biological plausibility, of cases, reporting rate, and health consequences to find important drug-AE associations. In 2011, the ground work for the likelihood ratio test (LRT)-based methods LRT was done in a research paper by Huang et al. in volume 106 of the Journal of the American Statistical Association. The unique feature of this methodology when compared with the other data mining methods for safety exploration is that this method can control false positive signals at a pre-specified threshold and has high precision.

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