经济模型类型何时成为卫生技术评估中的决定性因素？从复发-缓解型多发性硬化症治疗选择扩展中学习

数据集获取（Figshare）

• 源站标题：When does economic model type become a decisive factor in health technology appraisals? Learning from the expanding treatment options for relapsing–remitting multiple sclerosis
• DOI：10.6084/m9.figshare.6752549.v1
• 发布日期：2018-07-05
• 文件数：2 个
• 文件总体积：746.79 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/When_does_economic_model_type_become_a_decisive_factor_in_health_technology_appraisals_Learning_from_the_expanding_treatment_options_for_relapsing_remitting_multiple_sclerosis/6752549

命令行下载

curl -L -o "ijme_a_1491007_sm2165.pdf" "https://ndownloader.figshare.com/files/12313283"
curl -L -o "ijme_a_1491007_sm2164.docx" "https://ndownloader.figshare.com/files/12313286"

文件清单

• ijme_a_1491007_sm2165.pdf（652.43 KB）
• ijme_a_1491007_sm2164.docx（94.35 KB）

文件总体积 746.79 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Objectives: Specific economic model types often become de facto standard for health technology appraisal over time. Markov and discrete event simulation (DES) models were compared to investigate the impact of innovative modeling on the cost-effectiveness of disease-modifying therapies (DMTs) in relapsing–remitting multiple sclerosis (RRMS). Fingolimod was compared to dimethyl fumarate (DMF; in highly active [HA] RRMS), alemtuzumab (in HA RRMS) and natalizumab (in rapidly evolving severe RRMS). Comparator DMTs were chosen to reflect different dosing regimens.
Materials and methods: Markov and DES models used have been published previously. Inputs were aligned in all relevant respects, with differences in the modeling of event-triggered attributes, such as relapse-related retreatment, which is inherently difficult with a memoryless Markov approach. Outcomes were compared, with and without different attributes.
Results: All results used list prices. For fingolimod and DMF, incremental cost-effectiveness ratios (ICERs) were comparable (Markov: £4206/quality-adjusted life year [QALY] gained versus DES: £3910/QALY gained). Deviations were observed when long-term adverse events (AEs) were incorporated in the DES (Markov: £25,412 saved/QALY lost, versus DES: £34,209 saved/QALY lost, fingolimod versus natalizumab; higher ICERs indicate greater cost-effectiveness). For fingolimod versus alemtuzumab, when relapse-triggered retreatment was included in the DES, large cost differences were observed (difference between incremental cost is £35,410 and QALY is 0.10).
Limitations: UK payer perspective, therefore societal approach was not considered. Resource utilization and utilities for both models were not derived from the subpopulations; as the focus is on model type, input limitations that apply to both models are less relevant.
Conclusions: Whilst no model can fully represent a disease, a DES allows an opportunity to include features excluded in a Markov structure. A DES may be more suitable for modeling in RRMS for health technology assessment purposes given the complexity of some DMTs. This analysis highlights the capabilities of different model structures to model eve

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.6752549.v1（source: https://tandf.figshare.com/articles/dataset/When_does_economic_model_type_become_a_decisive_factor_in_health_technology_appraisals_Learning_from_the_expanding_treatment_options_for_relapsing_remitting_multiple_sclerosis/6752549）。