Unmeasured confounding in nonrandomized studies: quantitative bias analysis in health technology assessment

被引：16

作者：

Leahy, Thomas P. ^{[1
]}

Kent, Seamus ^{[2
]}

Sammon, Cormac ^{[1
]}

Groenwold, Rolf H. H. ^{[3
,4
]}

Grieve, Richard ^{[5
]}

Ramagopalan, Sreeram ^{[6
]}

Gomes, Manuel ^{[7
]}

机构：

[1] PHMR Ltd, Westport F28 ET85, Ireland

[2] Natl Inst Hlth & Care Excellence, Manchester M1 4BT, Lancs, England

[3] Leiden Univ, Dept Clin Epidemiol, Med Ctr, Einthovenweg 20, NL-2333 Leiden, Netherlands

[4] Leiden Univ, Dept Biomed Data Sci, Med Ctr, Einthovenweg 20, NL-2333 Leiden, Netherlands

[5] London Sch Hyg & Trop Med, Dept Hlth Serv Res & Policy, London WC1E 7HT, England

[6] F Hoffmann La Roche, Global Access, Grenzacherstr 124, CH-4070 Basel, Switzerland

[7] UCL, Dept Appl Hlth Res, London WC1E 6BT, England

来源：

JOURNAL OF COMPARATIVE EFFECTIVENESS RESEARCH | 2022年 / 11卷 / 12期

关键词：

HTA; nonrandomized; quantitative bias analysis; unmeasured confounding; BAYESIAN SENSITIVITY-ANALYSIS; EXTERNAL ADJUSTMENT; IMPACT; FORMULAS; OUTCOMES; MODEL;

D O I：

10.2217/cer-2022-0029

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Evidence generated from nonrandomized studies (NRS) is increasingly submitted to health technology assessment (HTA) agencies. Unmeasured confounding is a primary concern with this type of evidence, as it may result in biased treatment effect estimates, which has led to much criticism of NRS by HTA agencies. Quantitative bias analyses are a group of methods that have been developed in the epidemiological literature to quantify the impact of unmeasured confounding and adjust effect estimates from NRS. Key considerations for application in HTA proposed in this article reflect the need to balance methodological complexity with ease of application and interpretation, and the need to ensure the methods fit within the existing frameworks used to assess nonrandomized evidence by HTA bodies.

引用

页码：851 / 859

页数：9

共 55 条

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