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不漏掉任何一项研究:一项非小细胞肺癌的网络荟萃分析表明考虑所有相关数据的重要性。

No study left behind: a network meta-analysis in non-small-cell lung cancer demonstrating the importance of considering all relevant data.

作者信息

Hawkins Neil, Scott David A, Woods Beth S, Thatcher Nicholas

机构信息

Oxford Outcomes Ltd., Oxford, UK.

出版信息

Value Health. 2009 Sep;12(6):996-1003. doi: 10.1111/j.1524-4733.2009.00541.x. Epub 2009 Apr 23.

Abstract

OBJECTIVE

To demonstrate the importance of considering all relevant indirect data in a network meta-analysis of treatments for non-small-cell lung cancer (NSCLC).

METHODS

A recent National Institute for Health and Clinical Excellence appraisal focussed on the indirect comparison of docetaxel with erlotinib in second-line treatment of NSCLC based on trials including a common comparator. We compared the results of this analysis to a network meta-analysis including other trials that formed a network of evidence. We also examined the importance of allowing for the correlations between the estimated treatment effects that can arise when analysing such networks.

RESULTS

The analysis of the restricted network including only trials of docetaxel and erlotinib linked via the common placebo comparator produced an estimated mean hazard ratio (HR) for erlotinib compared with docetaxel of 1.55 (95% confidence interval [CI] 0.72-2.97). In contrast, the network meta-analysis produced an estimated HR for erlotinib compared with docetaxel of 0.83 (95% CI 0.65-1.06). Analyzing the wider network improved the precision of estimated treatment effects, altered their rankings and also allowed further treatments to be compared. Some of the estimated treatment effects from the wider network were highly correlated.

CONCLUSIONS

This empirical example shows the importance of considering all potentially relevant data when comparing treatments. Care should therefore be taken to consider all relevant information, including correlations induced by the network of trial data, when comparing treatments.

摘要

目的

证明在非小细胞肺癌(NSCLC)治疗的网络荟萃分析中考虑所有相关间接数据的重要性。

方法

最近一项英国国家卫生与临床优化研究所的评估聚焦于基于包含共同对照的试验,在NSCLC二线治疗中多西他赛与厄洛替尼的间接比较。我们将该分析结果与一项纳入其他试验形成证据网络的网络荟萃分析结果进行比较。我们还研究了在分析此类网络时考虑估计治疗效果之间相关性的重要性。

结果

仅包括通过共同安慰剂对照关联的多西他赛和厄洛替尼试验的受限网络分析得出,与多西他赛相比,厄洛替尼的估计平均风险比(HR)为1.55(95%置信区间[CI]0.72 - 2.97)。相比之下,网络荟萃分析得出与多西他赛相比厄洛替尼的估计HR为0.83(95%CI 0.65 - 1.06)。分析更广泛的网络提高了估计治疗效果的精确度,改变了它们的排名,还使得可以比较更多治疗方法。更广泛网络中的一些估计治疗效果高度相关。

结论

这个实例表明在比较治疗方法时考虑所有潜在相关数据的重要性。因此,在比较治疗方法时应谨慎考虑所有相关信息,包括试验数据网络引起的相关性。

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