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在临床试验分析中纳入历史数据:是否值得付出努力?

Including historical data in the analysis of clinical trials: Is it worth the effort?

机构信息

1 Department of Biostatistics, Erasmus University Medical Center, Rotterdam, the Netherlands.

2 F. Hoffmann-La Roche, Basel, Switzerland.

出版信息

Stat Methods Med Res. 2018 Oct;27(10):3167-3182. doi: 10.1177/0962280217694506. Epub 2017 Feb 21.

Abstract

Data of previous trials with a similar setting are often available in the analysis of clinical trials. Several Bayesian methods have been proposed for including historical data as prior information in the analysis of the current trial, such as the (modified) power prior, the (robust) meta-analytic-predictive prior, the commensurate prior and methods proposed by Pocock and Murray et al. We compared these methods and illustrated their use in a practical setting, including an assessment of the comparability of the current and the historical data. The motivating data set consists of randomised controlled trials for acute myeloid leukaemia. A simulation study was used to compare the methods in terms of bias, precision, power and type I error rate. Methods that estimate parameters for the between-trial heterogeneity generally offer the best trade-off of power, precision and type I error, with the meta-analytic-predictive prior being the most promising method. The results show that it can be feasible to include historical data in the analysis of clinical trials, if an appropriate method is used to estimate the heterogeneity between trials, and the historical data satisfy criteria for comparability.

摘要

先前具有类似设置的试验数据在临床试验分析中通常是可用的。已经提出了几种贝叶斯方法,以便将历史数据作为当前试验分析的先验信息,例如(修正的)功效先验、(稳健的)荟萃分析预测先验、相称先验和 Pocock 和 Murray 等人提出的方法。我们比较了这些方法,并在实际环境中说明了它们的使用,包括对当前数据和历史数据的可比性的评估。激励数据集由急性髓系白血病的随机对照试验组成。使用模拟研究来比较这些方法在偏差、精度、功效和Ⅰ型错误率方面的性能。估计试验间异质性参数的方法通常可以在功效、精度和Ⅰ型错误率之间提供最佳的权衡,荟萃分析预测先验是最有前途的方法。结果表明,如果使用适当的方法来估计试验间的异质性,并且历史数据满足可比性标准,那么在临床试验分析中纳入历史数据是可行的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/478f/6176344/460566b78ca1/10.1177_0962280217694506-fig1.jpg

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