Suppr超能文献

理解独特的患者因果单一论和患者报告的结果。

Understanding the unique patient-causal singularism and patient reported outcomes.

作者信息

Meadows K A, Reaney M

机构信息

Health Outcomes Insights Ltd, Oxfordshire, UK.

IQVIA Patient Centered Solutions, Reading, UK.

出版信息

Qual Life Res. 2025 May;34(5):1221-1231. doi: 10.1007/s11136-025-03905-2. Epub 2025 Jan 29.

Abstract

Patient reported outcome measures (PROMs) now play a significant role in randomized control trials (RCTs) providing the basis for efficacy or safety endpoints. Most PROM data is quantitative and is summarized at the group level. Whilst PROM data is informative in providing the aggregated patient perspective on disease and interventions, it provides little information about the patients' individual experiences. For this, qualitative 'case study' research is needed. However, qualitative case-study research has traditionally not been considered as robust for establishing causal inference due to its singular nature and lack of quantifiable findings. The focus of this paper was to advance a proposal as to how to produce a single mixed-methods case analysis of an individual's experiences with treatment from PROM and narrative data that can be used in causal inference research; so-called "Causal singularism".

摘要

患者报告结局测量(PROMs)如今在随机对照试验(RCTs)中发挥着重要作用,为疗效或安全性终点提供依据。大多数PROM数据是定量的,并在组水平上进行汇总。虽然PROM数据在提供患者对疾病和干预措施的总体看法方面具有参考价值,但它几乎没有提供关于患者个体经历的信息。为此,需要进行定性的“案例研究”。然而,由于定性案例研究的单一性和缺乏可量化的结果,传统上并不认为它在建立因果推断方面具有足够的说服力。本文的重点是提出一项建议,即如何对来自PROM和叙事数据的个体治疗经历进行单一的混合方法案例分析,以用于因果推断研究;即所谓的“因果单一论”。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验