Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Department of Medicine, Harvard Medical School, 1630 Tremont St Suite 303, Boston, MA 02120, USA; Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA.
J Clin Epidemiol. 2022 Nov;151:161-170. doi: 10.1016/j.jclinepi.2022.08.012. Epub 2022 Sep 6.
Multiple database studies on the same question, conducted by different investigators using different approaches or different data sources, can be considered sensitivity analyses for the same causal treatment effect question. We evaluated the contribution of alternative study design parameters and analysis choices to variation in estimates of the risk of major bleeding with dabigatran compared with warfarin.
We followed a 7-step process: (1) identify published studies asking the same question, (2) independently reproduce selected studies in the same data sources as the original authors, (3) contact original authors, (4) evaluate validity, (5) document critical study parameter specifications, (6) implement a designed matrix of variations in study parameters based on the original studies, and (7) evaluate contributors to variation in results.
Most variation remained unexplained (60-88%). Of the explained variation, two-thirds were related to data and population differences, and one-third were related to the use of alternative study design and analysis parameters. Among these, the most prominent were differences in outcome algorithms and criteria used to define follow-up.
When making policy decisions based on database study findings, it is important to evaluate the validity, consistency, and robustness of results to alternative design and analysis decisions.
针对同一问题，不同研究者采用不同方法或不同数据源进行的多项数据库研究可被视为同一因果治疗效果问题的敏感性分析。我们评估了替代研究设计参数和分析选择对达比加群酯与华法林相比大出血风险估计值的变化的影响。
我们遵循了 7 个步骤：（1）确定提出相同问题的已发表研究，（2）在与原始作者相同的数据来源中独立复制选定的研究，（3）联系原始作者，（4）评估有效性，（5）记录关键研究参数规范，（6）根据原始研究实施研究参数设计矩阵的变化，以及（7）评估结果变化的原因。
大多数差异仍然无法解释（60-88%）。在可解释的差异中，三分之二与数据和人群差异有关，三分之一与替代研究设计和分析参数的使用有关。其中，最突出的是结局算法和定义随访的标准存在差异。
基于数据库研究结果做出决策时，评估结果对替代设计和分析决策的有效性、一致性和稳健性非常重要。
