Wu Yujie, Wu Xiao, Gail Mitchell H, Ziegler Regina G, Smith-Warner Stephanie A, Wang Molin
Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA.
Department of Biostatistics, Columbia Mailman School of Public Health, New York, NY.
ArXiv. 2025 May 4:arXiv:2505.02220v1.
Pooled analyses that aggregate data from multiple studies are becoming increasingly common in collaborative epidemiologic research in order to increase the size and diversity of the study population. However, biomarker measurements from different studies are subject to systematic measurement errors and directly pooling them for analyses may lead to biased estimates of the regression parameters. Therefore, study-specific calibration processes must be incorporated in the statistical analyses to address between-study/assay/laboratory variability in the biomarker measurements. We propose a likelihood-based method to evaluate biomarker-disease relationships for categorical biomarkers in matched/nested case-control studies. To account for the additional uncertainties from the calibration processes, we propose a sandwich variance estimator to obtain valid asymptotic variances of the estimated regression parameters. Extensive simulation studies with varying sample sizes and biomarker-disease associations are used to evaluate the finite sample performance of our proposed methods. As an illustration, we apply the methods to a vitamin D pooling project of colorectal cancer to evaluate the effect of categorical vitamin D levels on colorectal cancer risks.
在协作性流行病学研究中，汇总来自多项研究数据的合并分析越来越普遍，目的是增加研究人群的规模和多样性。然而，不同研究中的生物标志物测量存在系统测量误差，直接将它们合并进行分析可能会导致回归参数的估计出现偏差。因此，在统计分析中必须纳入针对特定研究的校准过程，以解决生物标志物测量中研究间/检测方法/实验室的变异性问题。我们提出一种基于似然的方法，用于评估匹配/巢式病例对照研究中分类生物标志物与疾病的关系。为了考虑校准过程带来的额外不确定性，我们提出一种三明治方差估计器，以获得估计回归参数的有效渐近方差。我们使用具有不同样本量和生物标志物与疾病关联的广泛模拟研究，来评估我们提出的方法的有限样本性能。作为示例，我们将这些方法应用于一项结直肠癌维生素D合并项目，以评估分类维生素D水平对结直肠癌风险的影响。
