| 引用本文: | 余玺辉,陈坚,乔志坤,陈静,何敬成,莫小兰.基于ARIMA模型对集采药品环孢素口服制剂的科学报量[J].中国现代应用药学,2025,42(7):147-146. |
| YU Xihui,CHEN Jian,QIAO Zhikun,CHEN Jing,HE Jingcheng,MO Xiaolan.Scientific Analysis of the Consumption of Oral Preparations of Cyclosporin Based on ARIMA Model[J].Chin J Mod Appl Pharm(中国现代应用药学),2025,42(7):147-146. |
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| 摘要: |
| 目的 基于时间序列的分析方法，探讨ARIMA模型预测集采药品环孢素口服制剂用量的可行性，为科学预测集采药品报量提供参考。方法 应用R语言和SPSS对2018年1月至2021年12月环孢素口服制剂用量的用药数据建立最优的ARIMA模型，经模型参数识别、校验后，对数据进行拟合，并验证拟合效果。结果 环孢素口服制剂用量拟合效果最佳的模型为ARIMA(0,1,1)(0,1,0)[12]。2022年预测值与实际值的相对误差为3.13%，证明模型拟合效果很好。结论 ARIMA模型能够很好地拟合和预测环孢素口服制剂用量在时间序列上的变化趋势，可为集采的科学报量提供一定的参考。 |
| 关键词: 时间序列分析 ARIMA 带量采购 科学报量 环孢素 |
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| Scientific Analysis of the Consumption of Oral Preparations of Cyclosporin Based on ARIMA Model |
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YU Xihui1, CHEN Jian2,3,4, QIAO Zhikun1, CHEN Jing1, HE Jingcheng5, MO Xiaolan1
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1.Guangzhou Women and Children’s Medical Center;2.Shunde Women and Children'3.'4.s Hospital of Guangdong Medical University (Maternity and Child Healthcare Hospital of Shunde Foshan);5.Shunde Hospital of Southern Medical University
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| Abstract: |
| OBJECTIVE A Based on time series analysis methods, we explore the feasibility of predicting the consumption of oral preparations of cyclosporin by ARIMA model, which is provide reference for scientific prediction of drug procurement with target quantity. METHODS R language and SPSS were used to establish the optimal ARIMA model for the data of irbesartan tablets from January 2018 to December 2021. After model parameter identification and verification, the data was fitted and the fitting effect was verified. RESULTS ARIMA(0,1,1)(0,1,0)[12] had the best fitting effect on the consumption of irbesartan tablets. The relative error between the predicted value and the actual value in 2022 is 3.13%, proving that the model has a good fitting effect. CONCLUSION The ARIMA model can well fit and predict the trend of dosage of oral preparations of cyclosporin in time series, and can provide a certain reference for scientific target quantity of centralized procurement. |
| Key words: analysis of time series ARIMA procurement with target quantity scientific target quantity ciclosporin |
