Group Leader Research Group of Jian Gong on Pharmacoepidemiology and Clinical Drug Evaluation, Shenyang Pharmaceutical University Shenyang, China (People's Republic)
Background: There are relatively few drugs on the market that are only available for children, and most drugs are approved without studies in children, resulting in a lack of information on safety, efficacy and dosage for children.
Objectives: Based on the analysis of the data from the US FDA database, this study proposed a combined prediction model based on ARIMA and linear regression to predict the number of pediatric drug label changes, in order to obtain the focus trend of pediatric drug use.
Methods: Literature search strategy The data used in this article were obtained from the US FDA Pediatric Label Change Database. Information on changes included in the database from 2001 to 2021 was collected through searches. Inclusion criteria Drugs that have been approved for marketing in children in the United States between 2001 and 2021 and have undergone drug label changes. Statistical method Relevant data collection was carried out through the US FDA pediatric label database, and the information on drug label changes in children from 2001 to 2021 in the database was analyzed through the search function, and the World Health Organization Anatomical Therapeutic Chemistry System classification standard was used to classify drugs, and the amount of children's drug label changes was used to classify and summarize different types and different times of children's drugs. The compilation of drug statistics was compiled into Excel 2019 for descriptive statistical analysis, and a combined prediction model of ARIMA and linear regression was established.
Results: The combined prediction model of ARIMA and linear regression shows that the FDA pediatric label will increase further in the next 5 years. According to the predicted quantity, it can be inferred that many drugs will be evaluated in children in the future, so as to obtain more clinical data, so as to ensure the safe use of drugs in children and provide basis for rational use of drugs in children in the future.
Conclusions: The relevant data collection was carried out through the US FDA pediatric label database, the information of pediatric drug label changes in the database from 2001 to 2021 was analyzed through the search function, and the number of pediatric drug label changes in children was fitted and predicted based on the ARIMA and linear regression combination prediction model, and the linear regression model was combined with the ARIMA model by using the reciprocal method of squared and the prediction error, and the value of the FDA pediatric label change from 2001 to 2021 was calculated by the combination model. From the result analysis, the combined model is better than the prediction accuracy of the single model.
