An MCEM Framework for Drug Safety Signal Detection and Combination from Heterogeneous Real World Evidence.

ABSTRACT: Delayed drug safety insights can impact patients, pharmaceutical companies, and the whole society. Post-market drug safety surveillance plays a critical role in providing drug safety insights, where real world evidence such as spontaneous reporting systems (SRS) and a series of disproportional analysis serve as a cornerstone of proactive and predictive drug safety surveillance. However, they still face several challenges including concomitant drugs confounders, rare adverse drug reaction (ADR) detection, data bias, and the under-reporting issue. In this paper, we are developing a new framework that detects improved drug safety signals from multiple data sources via Monte Carlo Expectation-Maximization (MCEM) and signal combination. In MCEM procedure, we propose a new sampling approach to g

SUBMITTER: Xiao C 

PROVIDER: S-EPMC5789130 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature