计算生存数据采样的预期净收益：教程与案例研究

IF 3.1 3区医学 Q2 HEALTH CARE SCIENCES & SERVICES

Medical Decision Making Pub Date : 2024-10-01 Epub Date: 2024-09-20 DOI:10.1177/0272989X241279459

Mathyn Vervaart

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引用次数: 0

摘要

亮点：通过收集额外数据来减少决策不确定性的净值可以通过预期采样净收益（ENBS）来量化。本教程介绍了一种计算收集生存数据的 ENBS 的通用算法，以及在 R 中的逐步实现。该算法基于最近发布的模拟生存数据和计算样本信息期望值的方法，这些方法不依赖于生存数据遵循任何特定的参数分布，而且可以考虑任何任意的删减过程。我们通过一个基于先前癌症技术评估的案例研究证明，ENBS 计算不仅有助于设计新的研究，还有助于根据正在进行的试验中的不成熟证据优化新医疗技术的报销决策。

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Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study.

Highlights: The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R.The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process.We demonstrate in a case study based on a previous cancer technology appraisal that ENBS calculations are useful not only for designing new studies but also for optimizing reimbursement decisions for new health technologies based on immature evidence from ongoing trials.

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来源期刊

Medical Decision Making 医学-卫生保健

CiteScore

6.50

自引率

5.60%

发文量

146

审稿时长

6-12 weeks

期刊介绍： Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.