Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Medical School Office Building, 1265 Welch Road, Mail Code 5411, Stanford, CA 94305-5411, USA.
Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Medical School Office Building, 1265 Welch Road, Mail Code 5411, Stanford, CA 94305-5411, USA; Department of Health Research and Policy, 150 Governor's Lane, HRP Redwood Building, Stanford University School of Medicine, Stanford, CA 94305-5405 USA; Department of Statistics, Stanford University School of Humanities and Sciences, Sequoia Hall, Mail Code 4065, 390 Serra Mall, Stanford University, Stanford, CA 94305-4020, USA; Meta-Research Innovation Center at Stanford (METRICS), Stanford University, 1070 Arastradero Road, Palo Alto, CA 94304, USA.
J Clin Epidemiol. 2017 Aug;88:21-29. doi: 10.1016/j.jclinepi.2017.04.007. Epub 2017 Apr 21.
Meta-analyses of individual participant data (MIPDs) offer many advantages and are considered the highest level of evidence. However, MIPDs can be seriously compromised when they are not solidly founded upon a systematic review. These data-intensive collaborative projects may be led by experts who already have deep knowledge of the literature in the field and of the results of published studies and how these results vary based on different analytical approaches. If investigators tailor the searches, eligibility criteria, and analysis plan of the MIPD, they run the risk of reaching foregone conclusions. We exemplify this potential bias in a MIPD on the association of body mass index with mortality conducted by a collaboration of outstanding and extremely knowledgeable investigators. Contrary to a previous meta-analysis of group data that used a systematic review approach, the MIPD did not seem to use a formal search: it considered 239 studies, of which the senior author was previously aware of at least 238, and it violated its own listed eligibility criteria to include those studies and exclude other studies. It also preferred an analysis plan that was also known to give a specific direction of effects in already published results of most of the included evidence. MIPDs where results of constituent studies are already largely known need safeguards to their validity. These may include careful systematic searches, adherence to the Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data guidelines, and exploration of the robustness of results with different analyses. They should also avoid selective emphasis on foregone conclusions based on previously known results with specific analytical choices.
个体参与者数据的荟萃分析（MIPD）提供了许多优势，被认为是最高级别的证据。然而，如果没有基于系统综述的坚实基础，MIPD 可能会受到严重影响。这些数据密集型的合作项目可能由已经对该领域的文献和已发表研究的结果以及这些结果如何根据不同的分析方法而变化有深入了解的专家领导。如果研究人员调整 MIPD 的搜索、资格标准和分析计划，他们就有可能得出预先确定的结论。我们在一个由杰出且知识渊博的研究人员合作进行的关于体重指数与死亡率关联的 MIPD 中举例说明了这种潜在的偏见。与之前使用系统综述方法进行的群组数据荟萃分析相反，MIPD 似乎没有使用正式的搜索：它考虑了 239 项研究，其中至少有 238 项是资深作者之前已经知道的，并且它违反了自己列出的资格标准，包括这些研究并排除了其他研究。它还更喜欢一种分析计划，这种计划也被认为会在已经包含的大多数证据的已发表结果中给出特定的效果方向。对于那些组成研究的结果已经基本已知的 MIPD，需要对其有效性进行保护。这些措施可能包括仔细的系统搜索、遵守个体参与者数据系统综述和荟萃分析的首选报告项目的规定，以及用不同的分析方法探索结果的稳健性。它们还应避免基于特定分析选择的先前已知结果选择性地强调预先确定的结论。
