Lu Jinmiao, Wang Guangfei, Ying Xiaohua, Li Zhiping
Department of Pharmacy, Children's Hospital of Fudan University, Shanghai, China.
NHC Key Laboratory of Health Technology Assessment, Department of Health Economics, School of Public Health, Fudan University, Shanghai, China.
Technol Health Care. 2023;31(2):691-703. doi: 10.3233/THC-220355.
The medicine selection method is a critical and challenging issue in medical insurance decision-making.
This study proposed a real-world data-based multi-criteria decision analysis (MCDA) model with a hybrid entropic weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithms to select satisfactory drugs.
The evaluation index includes two levels: primary criteria and sub-criteria. Firstly, we proposed six primary criteria to form the value health framework. The primary criteria's weights were derived from the policymakers' questionnaire. Meanwhile, clinically relevant sub-criteria were derived from high-quality (screened by GRADE scores) clinical-research literature. Their weights are determined by the entropy weight (EW) algorithm. Secondly, we split the primary criteria into six mini-EW-TOPSIS models. Then, we obtained six ideal closeness degree scores (ICDS) for each candidate drug. Thirdly, we get the total utility score by linear weighting the ICDS. The higher the utility score, the higher the ranking.
A national multicenter real-world case study of the ranking of four generic antibiotics validated the proposed model. This model is verified by comparative experiments and sensitivity analysis. The whole ranking model was consistent and reliable. Based on these results, medical policymakers can intuitively and easily understand the characteristics of each drug to facilitate follow-up drug policy-making.
The ranking algorithm combines the objective characteristics of medicine and policy makers' opinions, which can improve the applicability of the results. This model can help decision-makers, clinicians, and related researchers better understand the drug assessment process.
药品选择方法是医疗保险决策中的一个关键且具有挑战性的问题。
本研究提出一种基于真实世界数据的多标准决策分析（MCDA）模型，该模型采用混合熵权法与理想解贴近度排序法（TOPSIS）算法来选择令人满意的药物。
评估指标包括两个层次：一级标准和二级标准。首先，我们提出六个一级标准以构建价值健康框架。一级标准的权重来自政策制定者的调查问卷。同时，临床相关的二级标准来自高质量（通过GRADE评分筛选）的临床研究文献。它们的权重由熵权（EW）算法确定。其次，我们将一级标准拆分为六个小型EW-TOPSIS模型。然后，我们为每种候选药物获得六个理想贴近度得分（ICDS）。第三，通过对ICDS进行线性加权得到总效用得分。效用得分越高，排名越高。
一项关于四种通用抗生素排名的全国多中心真实世界案例研究验证了所提出的模型。该模型通过对比实验和敏感性分析得到验证。整个排名模型具有一致性和可靠性。基于这些结果，医疗政策制定者可以直观且轻松地了解每种药物的特性，以促进后续的药物政策制定。
该排名算法结合了药品的客观特征和政策制定者的意见，能够提高结果的适用性。此模型可以帮助决策者、临床医生和相关研究人员更好地理解药物评估过程。
