Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02120, USA.
Pharmacoepidemiol Drug Saf. 2010 Aug;19(8):848-57. doi: 10.1002/pds.1867.
Mandated post-marketing drug safety studies require vast databases pooled from multiple administrative data sources which can contain private and proprietary information. We sought to create a method to conduct pooled analyses while keeping information private and allowing for full confounder adjustment.
We propose a method based on propensity score (PS) techniques. A set of propensity scores are computed in each data-contributing center and a PS-adjusted analysis is then carried out on a pooled basis. The method is demonstrated in a study of the potentially negative effects of concurrent initiation of clopidogrel and proton pump inhibitors (PPIs) in four cohorts of patients assembled from North American claims data sources. Clinical outcomes were myocardial infarction (MI) hospitalization and hospitalization for revascularization procedure. Success of the method was indicated by equivalent performance of our PS-based method and traditional confounder adjustment. We also implemented and evaluated high-dimensional propensity scores and meta-analytic techniques.
On both a pooled and individual cohort basis, we saw substantially similar point estimates and confidence intervals for studies adjusted by covariates and from privacy-maintaining propensity scores. The pooled, adjusted OR for MI hospitalization was 1.20 (95% confidence interval 1.03, 1.41) with individual variable adjustment and 1.16 (1.00, 1.36) with PS adjustment. The revascularization OR estimates differed by < 1%. Meta-analysis and pooling yielded substantially similar results.
We observed little difference in point estimates when we employed standard techniques or the proposed privacy-maintaining pooling method. We would recommend the technique in instances where multi-center studies require both privacy and multivariate adjustment.
强制性上市后药物安全性研究需要从多个管理数据源中汇集庞大的数据库，这些数据库可能包含私人和专有信息。我们旨在创建一种方法，在保持信息隐私的同时进行汇总分析，并允许充分调整混杂因素。
我们提出了一种基于倾向评分（PS）技术的方法。在每个提供数据的中心计算一组倾向评分，然后在汇总的基础上进行 PS 调整分析。该方法在来自北美索赔数据来源的四个患者队列中研究氯吡格雷和质子泵抑制剂（PPIs）同时起始的潜在负面影响中得到了证明。临床结局是心肌梗死（MI）住院和血运重建程序住院。我们的 PS 基于方法和传统混杂因素调整的表现相当，表明该方法是成功的。我们还实施并评估了高维倾向评分和荟萃分析技术。
无论是在汇总还是个别队列的基础上，我们都看到了通过协变量调整和保持隐私的倾向评分进行研究的结果大致相似。调整后的 MI 住院调整后比值比（OR）为 1.20（95%置信区间 1.03，1.41），个体变量调整为 1.16（1.00，1.36）。血管重建 OR 估计值相差<1%。荟萃分析和汇总产生了非常相似的结果。
当我们使用标准技术或建议的隐私保护汇总方法时，我们观察到点估计值几乎没有差异。我们将推荐该技术用于需要隐私和多变量调整的多中心研究。
