Wheaton Lorna, Gsteiger Sandro, Hubbard Stephanie, Bujkiewicz Sylwia
Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK.
Global Access, F Hoffman-La Roche AG, Basel, Switzerland.
BMC Med Res Methodol. 2025 Apr 2;25(1):86. doi: 10.1186/s12874-025-02507-3.
Meta-analysis is a useful method for combining evidence from multiple studies to detect treatment effects that could perhaps not be identified in a single study. While traditionally meta-analysis has assumed that populations of included studies are comparable, over recent years the development of precision medicine has led to identification of predictive genetic biomarkers which has resulted in trials conducted in mixed biomarker populations. For example, early trials may be conducted in patients with any biomarker status with no subgroup analysis, later trials may be conducted in patients with any biomarker status and subgroup analysis, and most recent trials may be conducted in biomarker-positive patients only. This poses a problem for traditional meta-analysis methods which rely on the assumption of somewhat comparable populations across studies. In this review, we provide a background to meta-analysis methods allowing for synthesis of data with mixed biomarker populations across trials.
For the methodological review, PubMed was searched to identify methodological papers on evidence synthesis for mixed populations. Several identified methods were applied to an illustrative example in metastatic colorectal cancer.
We identified eight methods for evidence synthesis of mixed populations where three methods are applicable to pairwise meta-analysis using aggregate data (AD), three methods are applicable to network meta-analysis using AD, and two methods are applicable to network meta-analysis using AD and individual participant data (IPD). The identified methods are described, including a discussion of the benefits and limitations of each method.
Methods for synthesis of data from mixed populations are split into methods which use (a) AD, (b) IPD, and (c) both AD and IPD. While methods which utilise IPD achieve superior statistical qualities, this is at the expense of ease of access to the data. Furthermore, it is important to consider the context of the decision problem in order to select the most appropriate modelling framework.
荟萃分析是一种有用的方法，可整合多项研究的证据，以发现单个研究中可能无法识别的治疗效果。传统上，荟萃分析假定纳入研究的人群具有可比性，但近年来，精准医学的发展导致了预测性遗传生物标志物的发现，这使得在混合生物标志物人群中开展试验。例如，早期试验可能在任何生物标志物状态的患者中进行，不进行亚组分析；后来的试验可能在任何生物标志物状态的患者中进行并进行亚组分析；而最近的试验可能仅在生物标志物阳性的患者中进行。这给传统的荟萃分析方法带来了问题，因为这些方法依赖于各研究人群在某种程度上具有可比性的假设。在本综述中，我们为荟萃分析方法提供了背景知识，这些方法允许对跨试验的混合生物标志物人群的数据进行综合分析。
对于方法学综述，检索了PubMed以确定关于混合人群证据综合的方法学论文。将几种已确定的方法应用于转移性结直肠癌的一个示例。
我们确定了八种混合人群证据综合的方法，其中三种方法适用于使用汇总数据（AD）的成对荟萃分析，三种方法适用于使用AD的网状荟萃分析，两种方法适用于使用AD和个体参与者数据（IPD）的网状荟萃分析。对所确定的方法进行了描述，包括对每种方法的优点和局限性的讨论。
混合人群数据综合的方法分为使用（a）AD、（b）IPD以及（c）AD和IPD两者的方法。虽然利用IPD的方法具有更好的统计质量，但这是以数据获取的便利性为代价的。此外，为了选择最合适的建模框架，考虑决策问题的背景很重要。
