Teramukai Satoshi, Matsuyama Yutaka, Mizuno Sachiko, Sakamoto Junichi
Division of Clinical Trial Design and Management, Translational Research Center, Kyoto University Hospital, Sakyo-ku, Kyoto 606-8507, Japan.
Jpn J Clin Oncol. 2004 Dec;34(12):717-21. doi: 10.1093/jjco/hyh138.
In meta-analyses of clinical trials, clinicians are often interested in examining subset effects. Meta-regression of aggregated data is a usual approach for relating sources of variation in treatment effects to specific study characteristics. However, it is known that study-level analyses can lead to biased assessments and have some limitations in explaining the heterogeneity. An individual patient data (IPD) meta-analysis offers several advantages for this purpose.
We compared some regression analyses of IPD with meta-regression analyses of the summarized data using a real-world example in order to investigate whether a binary patient characteristic is related to treatment effect. We used data from 10 randomized trials for non-small-cell lung cancer (n = 1355).
For treatment x stage interaction in IPD regression analysis, none of the tests of interactions was statistically significant. The meta-regression analysis gave a greater P-value than the IPD analysis. When excluding two studies, which had only stage I patients, the interaction was also not statistically significant in IPD analysis. On the other hand, the result of meta-regression analysis, though also showing no significant relationship, revealed a clear reversal in the direction of effect.
We suggest that the results of meta-regression analyses would not be as robust as those of regression analyses using IPD in examining potential modifiers of treatment effects. To investigate whether patient characteristics are related to treatment effects, we suggest that interaction tests and sensitivity analyses using IPD should be employed whenever possible.
在临床试验的荟萃分析中，临床医生常常对检验亚组效应感兴趣。对汇总数据进行的元回归是将治疗效果的变异来源与特定研究特征相关联的常用方法。然而，众所周知，研究层面的分析可能导致有偏差的评估，并且在解释异质性方面存在一些局限性。个体患者数据（IPD）荟萃分析在此方面具有若干优势。
我们通过一个实际例子比较了IPD的一些回归分析与汇总数据的元回归分析，以调查一种二元患者特征是否与治疗效果相关。我们使用了来自10项非小细胞肺癌随机试验的数据（n = 1355）。
在IPD回归分析中，对于治疗×分期的交互作用，没有一项交互作用检验具有统计学显著性。元回归分析给出的P值比IPD分析更大。当排除仅包含I期患者的两项研究时，IPD分析中的交互作用也无统计学显著性。另一方面，元回归分析的结果虽然也显示无显著关系，但在效应方向上出现了明显反转。
我们认为，在检验治疗效果的潜在修饰因素时，元回归分析的结果不如使用IPD的回归分析结果稳健。为了调查患者特征是否与治疗效果相关，我们建议尽可能采用使用IPD的交互作用检验和敏感性分析。
