Safety of interferon β-1a for treatment of COVID-19： a real-world study based on FAERS database / 上海预防医学

ObjectiveTo systematically evaluate the safety of interferon β-1a for treatment of corona virus disease 2019 （COVID-19）， and to provide references for interferon β-1a's clinical application. MethodsThis study was conducted with the database from US Food and Drug Administration adverse event reporting system （FAERS） from January 1， 2015 to March 31， 2021. Information component （IC） and reporting odds ratio （ROR） methods were applied for signal mining. ResultsA total of 463 700 records of COVID-19 were selected for analysis， and 45 positive drug adverse event signals were detected. Headache （IC025=1.09， ROR025=2.28）， pyrexia （IC025=0.51， ROR025=1.51） and multiple sclerosis relapse （IC025=3.67， ROR025=14.71） were positive adverse events with higher frequency. Autoimmune disorder （IC025=4.42， ROR025=24.03）， streptococcal infection （IC025=4.12， ROR025=19.82）， and multiple sclerosis relapse （IC025=3.67， ROR025=14.71） were positive adverse events. Acute lung injury， cardio-respiratory arrest and metabolic acidosis were associated with a higher proportion and frequency of death. ConclusionThere are certain safety issues with interferon β-1a in the treatment of COVID-19， and some adverse events with high frequency and high death rate deserve further attention by medical staffs.

Introduction of joint model and its applications in medical research / 中华流行病学杂志

In medical follow-up studies, longitudinal data and survival data are often accompanied and associated with each other, thus respective analysis of longitudinal and survival data might lead to biased results. Joint model can correct deviations, improve the efficiency of parameter estimation and provide effective inferences by simultaneously processing longitudinal and survival data. It is a popular method in medical research. Joint model has made much progress, whereas the literature about the joint model and its application is limited in China. This paper summarizes the main idea, basic framework, parameter estimation methods of random effect joint model and introduces the analysis on AIDS data set based on the R software package 'JM’ to clarify the advantages of the joint model in processing medical follow-up data and promote the use of the joint model in clinical research.