Pingping Li, Min Zhao, Xiangyu Liu, Hualing Yan, Zeying Yang, Chen Jiang, Yihe Tian, Hongchao Li
{"title":"经济评估中模型验证的现行做法:肿瘤方面的系统回顾","authors":"Pingping Li, Min Zhao, Xiangyu Liu, Hualing Yan, Zeying Yang, Chen Jiang, Yihe Tian, Hongchao Li","doi":"10.54844/hd.2024.0010","DOIUrl":null,"url":null,"abstract":"Objective: Model validation is crucial for ensuring confidence in economic models, and guidelines emphasize the need for \nresearchers to validate the pharmacoeconomic models they construct. This systematic review summarizes current practices \nand challenges in model validation for neoplasm economic evaluations, providing recommendations for improvement. \nMethods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we \nsearched PubMed, MEDLINE, Embase, and ScienceDirect for model-based economic evaluations published between 2021 \nand 2023. The studies were screened and extracted by two researchers independently. The frequency of each model validation \ntype was assessed, along with descriptions of specific practices, validation outcomes, and adjustments made based on the \nvalidation results. \nResults: Among the final 362 articles, 139 (38%) conducted model validation. External validation was the most commonly used \nvalidation method (47%), calibrating the model and comparing simulated outcomes with real-world evidence. Face validation \n(45%) relied on insights from clinical experts and economists. Internal validation (19%) employed tools such as the incremental \nmixture importance sampling (IMIS) algorithm and TreeAge. Cross validation (9%) compared data from similar events, while \npredictive validation (4%) used long-term follow-up data. Of the 39 studies (28%) that reported validation results, none of them \nmade any adjustments based on the validation outcomes. The Assessment of the Validation Status of Health-Economic decision \nmodels (AdViSHE), a validation-assessment tool, was utilized in three studies for model validation. Additionally, 10% of economic \nevaluations lacked clear validity, while 96% had one to three validity dimensions, and only 4% had four to five dimensions. \nConclusion: Most studies had limited and brief model validation practices without comprehensive descriptions. Researchers \nare encouraged to employ multiple validation methods and provide detailed descriptions of their validation practices, results, \nand adjustments. Active development and utilization of model evaluation tools should be promoted among scholars. \nKey words: model validation, economic evaluation, current practices, systematic review","PeriodicalId":430023,"journal":{"name":"Health Decision","volume":"89 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Current practices of model validation in economic evaluation: A systematic review in neoplasms\",\"authors\":\"Pingping Li, Min Zhao, Xiangyu Liu, Hualing Yan, Zeying Yang, Chen Jiang, Yihe Tian, Hongchao Li\",\"doi\":\"10.54844/hd.2024.0010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective: Model validation is crucial for ensuring confidence in economic models, and guidelines emphasize the need for \\nresearchers to validate the pharmacoeconomic models they construct. This systematic review summarizes current practices \\nand challenges in model validation for neoplasm economic evaluations, providing recommendations for improvement. \\nMethods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we \\nsearched PubMed, MEDLINE, Embase, and ScienceDirect for model-based economic evaluations published between 2021 \\nand 2023. The studies were screened and extracted by two researchers independently. The frequency of each model validation \\ntype was assessed, along with descriptions of specific practices, validation outcomes, and adjustments made based on the \\nvalidation results. \\nResults: Among the final 362 articles, 139 (38%) conducted model validation. External validation was the most commonly used \\nvalidation method (47%), calibrating the model and comparing simulated outcomes with real-world evidence. Face validation \\n(45%) relied on insights from clinical experts and economists. Internal validation (19%) employed tools such as the incremental \\nmixture importance sampling (IMIS) algorithm and TreeAge. Cross validation (9%) compared data from similar events, while \\npredictive validation (4%) used long-term follow-up data. Of the 39 studies (28%) that reported validation results, none of them \\nmade any adjustments based on the validation outcomes. The Assessment of the Validation Status of Health-Economic decision \\nmodels (AdViSHE), a validation-assessment tool, was utilized in three studies for model validation. Additionally, 10% of economic \\nevaluations lacked clear validity, while 96% had one to three validity dimensions, and only 4% had four to five dimensions. \\nConclusion: Most studies had limited and brief model validation practices without comprehensive descriptions. Researchers \\nare encouraged to employ multiple validation methods and provide detailed descriptions of their validation practices, results, \\nand adjustments. Active development and utilization of model evaluation tools should be promoted among scholars. \\nKey words: model validation, economic evaluation, current practices, systematic review\",\"PeriodicalId\":430023,\"journal\":{\"name\":\"Health Decision\",\"volume\":\"89 4\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Decision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54844/hd.2024.0010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Decision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54844/hd.2024.0010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Current practices of model validation in economic evaluation: A systematic review in neoplasms
Objective: Model validation is crucial for ensuring confidence in economic models, and guidelines emphasize the need for
researchers to validate the pharmacoeconomic models they construct. This systematic review summarizes current practices
and challenges in model validation for neoplasm economic evaluations, providing recommendations for improvement.
Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we
searched PubMed, MEDLINE, Embase, and ScienceDirect for model-based economic evaluations published between 2021
and 2023. The studies were screened and extracted by two researchers independently. The frequency of each model validation
type was assessed, along with descriptions of specific practices, validation outcomes, and adjustments made based on the
validation results.
Results: Among the final 362 articles, 139 (38%) conducted model validation. External validation was the most commonly used
validation method (47%), calibrating the model and comparing simulated outcomes with real-world evidence. Face validation
(45%) relied on insights from clinical experts and economists. Internal validation (19%) employed tools such as the incremental
mixture importance sampling (IMIS) algorithm and TreeAge. Cross validation (9%) compared data from similar events, while
predictive validation (4%) used long-term follow-up data. Of the 39 studies (28%) that reported validation results, none of them
made any adjustments based on the validation outcomes. The Assessment of the Validation Status of Health-Economic decision
models (AdViSHE), a validation-assessment tool, was utilized in three studies for model validation. Additionally, 10% of economic
evaluations lacked clear validity, while 96% had one to three validity dimensions, and only 4% had four to five dimensions.
Conclusion: Most studies had limited and brief model validation practices without comprehensive descriptions. Researchers
are encouraged to employ multiple validation methods and provide detailed descriptions of their validation practices, results,
and adjustments. Active development and utilization of model evaluation tools should be promoted among scholars.
Key words: model validation, economic evaluation, current practices, systematic review