A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment.

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Medical Decision Making Pub Date : 2023-07-01 Epub Date: 2023-04-26 DOI:10.1177/0272989X231168618
Ash Bullement, Matthew D Stevenson, Gianluca Baio, Gemma E Shields, Nicholas R Latimer
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引用次数: 0

Abstract

Background: External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare.

Purpose: This review aims to identify, describe, and categorize established methods to incorporate external evidence into survival extrapolation for HTA.

Data sources: Embase, MEDLINE, EconLit, and Web of Science databases were searched to identify published methodological studies, supplemented by hand searching and citation tracking.

Study selection: Eligible studies were required to present a novel extrapolation approach incorporating external evidence (i.e., data or information) within survival model estimation.

Data extraction: Studies were classified according to how the external evidence was integrated as a part of model fitting. Information was extracted concerning the model-fitting process, key requirements, assumptions, software, application contexts, and presentation of comparisons with, or validation against, other methods.

Data synthesis: Across 18 methods identified from 22 studies, themes included use of informative prior(s) (n = 5), piecewise (n = 7), and general population adjustment (n = 9), plus a variety of "other" (n = 8) approaches. Most methods were applied in cancer populations (n = 13). No studies compared or validated their method against another method that also incorporated external evidence.

Limitations: As only studies with a specific methodological objective were included, methods proposed as part of another study type (e.g., an economic evaluation) were excluded from this review.

Conclusions: Several methods were identified in this review, with common themes based on typical data sources and analytical approaches. Of note, no evidence was found comparing the identified methods to one another, and so an assessment of different methods would be a useful area for further research.HighlightsThis review aims to identify methods that have been used to incorporate external evidence into survival extrapolations, focusing on those that may be used to inform health technology assessment.We found a range of different approaches, including piecewise methods, Bayesian methods using informative priors, and general population adjustment methods, as well as a variety of "other" approaches.No studies attempted to compare the performance of alternative methods for incorporating external evidence with respect to the accuracy of survival predictions. Further research investigating this would be valuable.

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关于将外部证据纳入健康技术评估中基于试验的生存推断的方法的系统性综述。
背景:外部证据通常用于为卫生技术评估(HTA)的生存模型提供信息。目的:本综述旨在确定、描述和归类将外部证据纳入 HTA 生存推断的既定方法:数据来源:对 Embase、MEDLINE、EconLit 和 Web of Science 数据库进行检索,以确定已发表的方法学研究,并辅以人工检索和引文追踪:符合条件的研究必须提出一种新的外推方法,将外部证据(即数据或信息)纳入生存模型估算中:数据提取:根据外部证据作为模型拟合一部分的整合方式对研究进行分类。提取的信息涉及模型拟合过程、关键要求、假设、软件、应用环境,以及与其他方法的比较或验证:在 22 项研究中确定的 18 种方法中,主题包括使用信息先验(n = 5)、片断(n = 7)和一般人群调整(n = 9),以及各种 "其他"(n = 8)方法。大多数方法都应用于癌症人群(n = 13)。没有研究将自己的方法与另一种也包含外部证据的方法进行比较或验证:由于只纳入了具有特定方法目标的研究,因此本综述排除了作为其他研究类型(如经济评估)一部分而提出的方法:本综述确定了几种方法,其共同主题基于典型的数据来源和分析方法。本综述旨在确定将外部证据纳入生存推断的方法,重点关注可用于健康技术评估的方法。我们发现了一系列不同的方法,包括计件方法、使用信息先验的贝叶斯方法、一般人群调整方法以及各种 "其他 "方法。对此进行进一步研究将是非常有价值的。
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来源期刊
Medical Decision Making
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.
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