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评估非劣效性:在数据不完整的情况下评估新疗法的疗效。

Assessing noninferiority: Evaluating efficacy of a new treatment without complete data.

作者信息

Gao Ping, Odem-Davis Katherine

机构信息

Brightech International, Somerset, New Jersey.

CFF-TDNCC, Seattle Children's Research Institute, Seattle, Washington.

出版信息

Pharm Stat. 2019 Oct;18(5):546-554. doi: 10.1002/pst.1946. Epub 2019 Apr 12.

Abstract

The FDA released the final guidance on noninferiority trials in November 2016. In noninferiority trials, validity of the assessment of the efficacy of the test treatment depends on the control treatment's efficacy. Therefore, it is critically important that there be a reliable estimate of the control treatment effect-which is generally obtained from historical trials, and often assumed to hold in the current setting (the assay constancy assumption). Validating the constancy assumption requires clinical data, which are typically lacking. The guidance acknowledges that "lack of constancy can occur for many reasons." We clarify the objectives of noninferiority trials. We conclude that correction for bias, rather than assay constancy, is critical to conducting valid noninferiority trials. We propose that assay constancy not be assumed and discounting or thresholds be used to address concern about loss of historical efficacy. Examples are provided for illustration.

摘要

美国食品药品监督管理局(FDA)于2016年11月发布了关于非劣效性试验的最终指南。在非劣效性试验中,受试治疗效果评估的有效性取决于对照治疗的效果。因此,对对照治疗效果进行可靠估计至关重要,这通常从既往试验中获得,并且常常假定在当前情况下仍然成立(即测定恒定性假设)。验证恒定性假设需要临床数据,但这些数据通常并不具备。该指南承认“恒定性缺失可能由多种原因导致”。我们阐明了非劣效性试验的目标。我们得出结论,对偏倚进行校正而非测定恒定性,对于开展有效的非劣效性试验至关重要。我们建议不要假定测定恒定性,并使用折扣或阈值来解决对既往疗效丧失的担忧。文中提供了示例以作说明。

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