Department of Neurosurgery, University of California San Diego, San Diego, CA, USA.
Center for Computational Analysis of Social and Organizational Systems, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Acta Neurochir (Wien). 2019 Feb;161(2):205-211. doi: 10.1007/s00701-019-03804-9. Epub 2019 Jan 23.
Our previous studies suggest that the training history of an investigator, termed "medical academic genealogy", influences the outcomes of that investigator's research. Here, we use meta-analysis and quantitative statistical modeling to determine whether such effects contribute to systematic bias in published conclusions.
A total of 108 articles were identified through a comprehensive search of the high-grade glioma (HGG) surgical resection literature. Analysis was performed on the 70 articles with sufficient data for meta-analysis. Pooled estimates were generated for key academic genealogies. Monte Carlo simulations were performed to determine whether the effects attributed to genealogy alone can arise due to chance alone.
Meta-analysis of the HGG literature without consideration for academic medical genealogy revealed that gross total resection (GTR) was associated with a significant decrease in the odds ratio (OR) for the hazard of death after surgery for both anaplastic astrocytoma (AA) and glioblastoma (AA: log [OR] = - 0.04, 95% CI [- 0.07 to - 0.01]; glioblastoma log [OR] = - 0.36, 95% CI [- 0.44 to - 0.29]). For the glioblastoma literature, meta-analysis of articles contributed by members of a genealogy consisting of mostly radiation oncologists revealed no reduction in the hazard of death after GTR [log [OR] = - 0.16, 95% CI [- 0.41 to 0.09]. In contrast, meta-analysis of published articles contributed by members of a genealogy consisting of mostly neurosurgeons revealed that GTR was associated with a significant reduction in the hazard of death [log [OR] = - 0.29, 95% CI [- 0.40 to 0.18]. Monte Carlo simulation revealed that the observed discrepancy between the articles contributed by the members of these two genealogies was unlikely to arise by chance alone (p < 0.006).
Meta-analysis of articles contributed by authors belonging to the different medical academic genealogies yielded distinct and contradictory pooled point-estimates, suggesting that genealogy contributes to systematic bias in the published literature.
我们之前的研究表明,调查员的培训历史,即“医学学术谱系”,会影响该调查员研究的结果。在这里,我们使用荟萃分析和定量统计建模来确定这些影响是否会导致已发表结论的系统偏差。
通过全面搜索高级别神经胶质瘤(HGG)手术切除文献,共确定了 108 篇文章。对具有足够荟萃分析数据的 70 篇文章进行了分析。对关键学术谱系进行了汇总估计。进行了蒙特卡罗模拟,以确定仅归因于谱系的影响是否仅因偶然而产生。
在不考虑医学学术谱系的情况下,对 HGG 文献进行荟萃分析显示,对于间变性星形细胞瘤(AA)和胶质母细胞瘤(AA:log [OR] = -0.04,95%CI [-0.07 至 -0.01];胶质母细胞瘤 log [OR] = -0.36,95%CI [-0.44 至 -0.29]),全切(GTR)与手术后死亡风险的优势比显著降低有关。对于胶质母细胞瘤文献,由主要由放射肿瘤学家组成的谱系的成员贡献的文章的荟萃分析显示,GTR 并没有降低全切后的死亡风险 [log [OR] = -0.16,95%CI [-0.41 至 0.09]。相比之下,由主要由神经外科医生组成的谱系的成员发表的文章的荟萃分析显示,GTR 与死亡风险的显著降低相关 [log [OR] = -0.29,95%CI [-0.40 至 0.18]。蒙特卡罗模拟显示,这两个谱系成员贡献的文章之间观察到的差异不太可能仅因偶然而产生(p <0.006)。
属于不同医学学术谱系的作者贡献的文章的荟萃分析产生了截然不同且相互矛盾的汇总点估计值,表明谱系会导致已发表文献中的系统偏差。