Dane Aaron, Spencer Amy, Rosenkranz Gerd, Lipkovich Ilya, Parke Tom
DaneStat Consulting, Macclesfield, UK.
Statistical Services Unit, University of Sheffield, Sheffield, UK.
Pharm Stat. 2019 Mar;18(2):126-139. doi: 10.1002/pst.1919. Epub 2018 Dec 27.
Subgroup by treatment interaction assessments are routinely performed when analysing clinical trials and are particularly important for phase 3 trials where the results may affect regulatory labelling. Interpretation of such interactions is particularly difficult, as on one hand the subgroup finding can be due to chance, but equally such analyses are known to have a low chance of detecting differential treatment effects across subgroup levels, so may overlook important differences in therapeutic efficacy. EMA have therefore issued draft guidance on the use of subgroup analyses in this setting. Although this guidance provided clear proposals on the importance of pre-specification of likely subgroup effects and how to use this when interpreting trial results, it is less clear which analysis methods would be reasonable, and how to interpret apparent subgroup effects in terms of whether further evaluation or action is necessary. A PSI/EFSPI Working Group has therefore been investigating a focused set of analysis approaches to assess treatment effect heterogeneity across subgroups in confirmatory clinical trials that take account of the number of subgroups explored and also investigating the ability of each method to detect such subgroup heterogeneity. This evaluation has shown that the plotting of standardised effects, bias-adjusted bootstrapping method and SIDES method all perform more favourably than traditional approaches such as investigating all subgroup-by-treatment interactions individually or applying a global test of interaction. Therefore, these approaches should be considered to aid interpretation and provide context for observed results from subgroup analyses conducted for phase 3 clinical trials.
在分析临床试验时,通常会进行按治疗交互作用的亚组评估,这对于3期试验尤为重要,因为试验结果可能会影响监管标签。此类交互作用的解读特别困难,一方面亚组研究结果可能是偶然的,但同样已知此类分析在检测不同亚组水平上的差异治疗效果方面机会较低,所以可能会忽略治疗效果的重要差异。因此,欧洲药品管理局(EMA)发布了关于在这种情况下使用亚组分析的指南草案。尽管该指南就预先设定可能的亚组效应的重要性以及在解读试验结果时如何使用这一点提出了明确建议,但对于哪些分析方法是合理的,以及如何根据是否需要进一步评估或采取行动来解读明显的亚组效应,却不太明确。因此,一个PSI/EFSPI工作组一直在研究一套有针对性的分析方法,以评估确证性临床试验中亚组间的治疗效果异质性,该研究考虑了所探索的亚组数量,还研究了每种方法检测此类亚组异质性的能力。该评估表明,标准化效应绘图、偏差调整自抽样法和SIDES法都比传统方法表现更优,传统方法如单独研究所有按治疗的亚组交互作用或应用交互作用的全局检验。因此,在解读3期临床试验进行的亚组分析的观察结果并提供背景信息时,应考虑这些方法。