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一种用于多发性硬化症的贝叶斯分层替代结局模型。

A Bayesian hierarchical surrogate outcome model for multiple sclerosis.

作者信息

Pozzi Luca, Schmidli Heinz, Ohlssen David I

机构信息

Division of Biostatistics, University of California Berkeley, Berkeley, 94720-7358, CA, USA.

Statistical Methodology, Development, Novartis Pharma AG, Basel, Switzerland.

出版信息

Pharm Stat. 2016 Jul;15(4):341-8. doi: 10.1002/pst.1749. Epub 2016 Apr 7.

DOI:10.1002/pst.1749
PMID:27061897
Abstract

The development of novel therapies in multiple sclerosis (MS) is one area where a range of surrogate outcomes are used in various stages of clinical research. While the aim of treatments in MS is to prevent disability, a clinical trial for evaluating a drugs effect on disability progression would require a large sample of patients with many years of follow-up. The early stage of MS is characterized by relapses. To reduce study size and duration, clinical relapses are accepted as primary endpoints in phase III trials. For phase II studies, the primary outcomes are typically lesion counts based on magnetic resonance imaging (MRI), as these are considerably more sensitive than clinical measures for detecting MS activity. Recently, Sormani and colleagues in 'Surrogate endpoints for EDSS worsening in multiple sclerosis' provided a systematic review and used weighted regression analyses to examine the role of either MRI lesions or relapses as trial level surrogate outcomes for disability. We build on this work by developing a Bayesian three-level model, accommodating the two surrogates and the disability endpoint, and properly taking into account that treatment effects are estimated with errors. Specifically, a combination of treatment effects based on MRI lesion count outcomes and clinical relapse was used to develop a study-level surrogate outcome model for the corresponding treatment effects based on disability progression. While the primary aim for developing this model was to support decision-making in drug development, the proposed model may also be considered for future validation. Copyright © 2016 John Wiley & Sons, Ltd.

摘要

多发性硬化症(MS)新型疗法的研发是临床研究各个阶段使用一系列替代结局的一个领域。虽然MS治疗的目的是预防残疾,但评估药物对残疾进展影响的临床试验需要大量患者样本并进行多年随访。MS的早期阶段以复发为特征。为了减少研究规模和持续时间,临床复发在III期试验中被用作主要终点。对于II期研究,主要结局通常是基于磁共振成像(MRI)的病灶计数,因为这些在检测MS活动方面比临床指标敏感得多。最近,索马尼及其同事在《多发性硬化症中扩展残疾状态量表(EDSS)恶化的替代终点》中进行了系统评价,并使用加权回归分析来检验MRI病灶或复发作为残疾试验水平替代结局的作用。我们在此基础上开展工作,开发了一个贝叶斯三级模型,纳入这两个替代结局和残疾终点,并适当考虑到治疗效果是有误差估计的。具体而言,基于MRI病灶计数结局和临床复发的治疗效果组合被用于开发基于残疾进展的相应治疗效果的研究水平替代结局模型。虽然开发此模型的主要目的是支持药物研发中的决策,但也可考虑对所提出的模型进行未来验证。版权所有© 2016约翰威立父子有限公司。

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A Bayesian hierarchical surrogate outcome model for multiple sclerosis.一种用于多发性硬化症的贝叶斯分层替代结局模型。
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