Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
Stat Med. 2019 Apr 15;38(8):1303-1320. doi: 10.1002/sim.8052. Epub 2018 Dec 19.
Pooling data from multiple studies improves estimation of exposure-disease associations through increased sample size. However, biomarker exposure measurements can vary substantially across laboratories and often require calibration to a reference assay prior to pooling. We develop two statistical methods for aggregating biomarker data from multiple studies: the full calibration method and the internalized method. The full calibration method calibrates all biomarker measurements regardless of the availability of reference laboratory measurements while the internalized method calibrates only non-reference laboratory measurements. We compare the performance of these two aggregation methods to two-stage methods. Furthermore, we compare the aggregated and two-stage methods when estimating the calibration curve from controls only or from a random sample of individuals from the study cohort. Our findings include the following: (1) Under random sampling for calibration, exposure effect estimates from the internalized method have a smaller mean squared error than those from the full calibration method. (2) Under the controls-only calibration design, the full calibration method yields effect estimates with the least bias. (3) The two-stage approaches produce average effect estimates that are similar to the full calibration method under a controls only calibration design and the internalized method under a random sample calibration design. We illustrate the methods in an application evaluating the relationship between circulating vitamin D levels and stroke risk in a pooling project of cohort studies.
从多个研究中汇总数据可以通过增加样本量来提高暴露与疾病关联的估计值。然而，生物标志物暴露测量值在不同实验室之间可能有很大差异，并且通常需要在汇总之前校准到参考测定法。我们开发了两种用于汇总来自多个研究的生物标志物数据的统计方法：完全校准方法和内部化方法。完全校准方法校准所有生物标志物测量值，无论是否有参考实验室测量值，而内部化方法仅校准非参考实验室测量值。我们将这两种聚合方法的性能与两阶段方法进行了比较。此外，我们比较了从对照中仅估计校准曲线或从研究队列中的随机个体样本中估计校准曲线时的聚合方法和两阶段方法。我们的研究结果包括以下内容：（1）在随机抽样进行校准的情况下，内部化方法的暴露效应估计值的均方误差小于完全校准方法的均方误差。（2）在仅对照校准设计中，完全校准方法产生的效应估计值偏差最小。（3）在仅对照校准设计下，两阶段方法产生的平均效应估计值与完全校准方法相似，而在随机样本校准设计下，与内部化方法相似。我们在一个应用中说明了这些方法，该应用评估了队列研究荟萃分析中循环维生素 D 水平与中风风险之间的关系。
